Analysis of medical publications from the standpoint of evidence-based medicine. Statistics as a tool for evidence-based medicine From user reviews

Lisa severe pain after operation. The clinician must choose between tablets based on external clinical evidence or injections based on personal clinical experience and patient preferences. The doctor knows that according to external clinical evidence, morphine tablets would be the best choice. However, as it turned out during the operation, Lisa suffers from a common side effect of anesthesia - vomiting. This means that if Lisa takes the pill and she vomits, the contents of the pill will come out and have no pain relief. Both the doctor and Lisa know from previous experience that Lisa may vomit within 30 minutes of the anesthetic wears off. Therefore, instead of a pill, the doctor decides to give Liza an injection with morphine.

In this example, the physician, based on personal clinical experience and the patient's preferences, decides to use the morphine injection instead of the morphine tablet, even though the best external clinical evidence is in favor of the latter. The doctor uses the same medical substance(i.e. morphine), as external clinical evidence suggests, but chooses another dosage form(an injection instead of a tablet).

This is an example of a doctor making a definite decision in the course of treatment, based on supporting evidence, after discussion with the patient.

What is evidence-based medicine?

(evidence-based medicine, EBM) is the process of systematically reviewing, evaluating and using the results of clinical trials in order to provide optimal medical care patients. Patients' awareness of evidence-based medicine has great importance because it allows them to make more informed decisions about the management and treatment of the disease. It also enables patients to form a more accurate picture of risk, encourages the appropriate use of individual procedures, and enables the clinician and/or patient to make decisions based on supporting evidence.

Evidence-based medicine combines principles and methods. Due to the operation of these principles and methods, decisions, instructions and strategies in medicine are based on current supporting data about efficiency different forms currents and medical services generally. For medicines, evidence-based medicine relies heavily on information obtained from benefit and risk assessments (efficacy and safety).

The concept of evidence-based medicine appeared in the 1950s. Up to this point, physicians have made decisions largely on the basis of their education, clinical experience, and reading scientific periodicals. However, studies have shown that medical treatment decisions vary significantly among different medical professionals. The basis for the introduction of systematic methods for collecting, evaluating and organizing research data was formed, which became the beginning of evidence-based medicine. The advent of evidence-based medicine has been recognized by physicians, pharmaceutical companies, regulators and the public.

The decision maker needs to rely on their own experience with patients, combined with the best supporting evidence from controlled trials and scientific developments. It is important in the decision-making process to combine clinical experience and controlled studies. In the absence of clinical experience Risk is the likelihood of harm or injury resulting from treatment in clinical practice or in research. Harm or injury can be physical, but also psychological, social or economic. Risks include developing side effects of treatment or taking a drug that is less effective than standard treatment (as part of a trial). When testing a new medicinal product side effects or other risks not foreseen by the investigators may occur. This situation is most typical for the initial stages of clinical trials.

Conducting any clinical trial involves risks. Participants should be informed about possible benefits and risks before deciding to participate (see definition of informed consent).

" target="_blank">The risk associated with certain treatments may result in unwanted effects.

Five-stage model of evidence-based medicine

One approach to evidence-based medicine involves a model of 5 stages:

1. formation of a clinically relevant request (search for information by a doctor to make a correct diagnosis),
2. search for better supporting data (doctor's search for supporting data in support of the information found in step 1),
3. assessment of the quality of supporting data (providing the doctor with high quality and reliability),
4. formation of a medical decision based on supporting data (acceptance by the patient and the doctor of an informed decision on treatment based on steps 1-3),
5. evaluation of the process (evaluation by the doctor and the patient of the achieved result and the corresponding adjustment of treatment decisions, if necessary).

In the example above, the choice of physician is consistent with both evidence-based medicine and patient feedback. The physician's decision involves the conscious, open and informed use of the best evidence available at the current time, including the experience of the patient, to select the best possible ways medical care for this patient.

Patient participation in the decision-making process is essential for the development of new treatment principles. Such participation includes reading and understanding treatment information and following recommendations consciously, working with clinical professionals to evaluate and select the best treatment options, and providing feedback on results. Patients can actively participate in the creation of supporting evidence at any level.

Assessment of supporting data for the needs of evidence-based medicine

The collected information is classified according to the level of supporting evidence it contains in order to assess its quality. The pyramid in the figure below shows different levels of evidence and their ranking.

levels of evidence

Comments or expert opinions

This is data based on the opinions of a panel of experts and aimed at forming a common medical practice.

Study of case series and descriptions of clinical cases

A case series study is a descriptive study of small people. As a rule, it serves as an addition or supplement to the description of a clinical case. A case report is a detailed report of the symptoms, signs, diagnosis, treatment and management of a single patient.

Case-control studies

is an observational retrospective study (with a review of historical data) in which patients suffering from a disease are compared with patients who do not have this disease. Cases such as lung cancer are usually studied in a case-control study. To do this, a group of smokers (group under the influence) and a group of non-smokers (group not under the influence) are recruited, which are monitored for a certain period of time. The difference in incidence of lung cancer is then documented, allowing a variable (the independent variable—in this case, smoking) to be considered as the cause of the dependent variable (in this case, lung cancer).

In this example, a significant increase in lung cancer cases in the smoking group compared to the non-smoking group is taken as evidence of a causal relationship between smoking and the occurrence of lung cancer.

cohort study

The modern definition of a cohort in a clinical trial is a group of individuals with certain characteristics who are monitored for health outcomes.

The Framingham Heart Study is an example of a cohort study conducted to answer an epidemiological question. The Framingham Study began in 1948 and is still ongoing. The purpose of the study is to investigate the impact of a number of factors on the incidence of heart disease. The question researchers are facing is whether high blood pressure, overweight, diabetes, physical activity and other factors are associated with the development of heart disease. For each of the exposure factors (such as smoking), researchers recruit a group of smokers (exposure group) and a non-smoker group (unexposed group). The groups are then observed over a period of time. Then, at the end of the observation period, the difference in the incidence of heart disease in these groups is documented. Groups are compared in terms of many other variables such as

• economic status (for example, education, income and occupation),
• health status (for example, the presence of other diseases).

This means that a variable (the independent variable, in this case, smoking) can be isolated as the cause of the dependent variable (in this case, lung cancer).

In this example, a statistically significant increase in the incidence of heart disease in the smoking group compared to the non-smoking group is taken as evidence of a causal relationship between smoking and the occurrence of heart disease. The results found in the Framingham Study over the years provide compelling evidence that cardiovascular disease is largely the result of measurable and modifiable risk factors, and that a person can control the health of their heart system if they carefully monitor their diet and lifestyle and refuses the consumption of refined fats, cholesterol and smoking, reduces weight or begins to lead an active lifestyle, regulates stress and blood pressure. It is largely due to the Framingham Study that we now have a clear understanding of the association of certain risk factors with heart disease.

Another example of a cohort study that has been running for many years is the National Child Development Study (NCDS), the most studied of all UK newborn cohort studies. The largest study on women is the Nurses Health Study. It began in 1976, the number of accompanied persons is more than 120 thousand people. According to this study, many diseases and outcomes were analyzed.

randomized clinical trials

Clinical trials are called randomized when randomization is used to allocate participants to different treatment groups. This means that treatment groups are randomly populated using a formal system, and there is a chance for each participant to get into each of the study destinations.

Meta-analysis

is a systematic, statistical-based review of data that compares and combines the results of different studies in order to identify patterns, inconsistencies, and other relationships across multiple studies. A meta-analysis can provide support for a stronger conclusion than any single study, but the disadvantages of bias due to the publication preference of positive study results must be kept in mind.

Study results

Outcome research is a broad umbrella term that does not have a fixed definition. Outcome research examines health care outcomes, in other words, the effect of the health care process on the health and well-being of patients. In other words, clinical outcome research aims to monitor, understand, and optimize the impact of medical treatment on a particular patient or group. Such studies describe scientific research that is related to the effectiveness of health measures and medical services, that is, the results obtained through such services.

Often attention is focused on the person suffering from the disease - in other words, on the clinical ( overall results) that are most relevant to that patient or group of patients. These endpoints can be either the degree pain. However, outcome studies may also focus on the effectiveness of health service delivery, with measures such as , health status, and disease severity (the impact of health problems on the individual).

The difference between evidence-based medicine and outcome research lies in the focus on different issues. While the main goal of evidence-based medicine is to provide the patient with optimal care in accordance with clinical evidence and experience, outcome studies are primarily aimed at predicting endpoints. In a clinical outcome study, these endpoints usually correspond to clinically relevant endpoints.

In outcome studies, the relevant endpoints are often symptoms or measures of functional ability and care needs that the patient receiving treatment considers important. For example, a patient suffering from an infection who has been injected with penicillin may pay more attention to the fact that he does not have high temperature and improved general condition than the impact of penicillin on actual infection rates. In this case, the symptoms and how he feels are seen as a direct measure of his state of health, and these are the endpoints that are the focus of the outcome study. The patient is also likely to be concerned about the possible side effects associated with penicillin as well as the cost of treatment. In the case of other diseases such as cancer, an important clinical outcome relevant to the patient will be the risk of death.

If the study is long in time, when studying the results of the studies, “ ” can be used. A surrogate endpoint involves the use of a biomarker to measure outcome, acting as a substitute for a clinical endpoint that measures the effect of penicillin by reducing the amount of a protein (C-reactive protein) that is always present in the blood. The amount of this protein in the blood healthy person very little, but acute infection it is rising rapidly. Thus, measuring the level of C-reactive protein in the blood is an indirect way to determine the presence of infection in the body, therefore, in this case, the protein serves as a "biomarker" of infection. A biomarker is a measurable indicator of a disease state. This parameter is also correlated with the risk of occurrence or progression of the disease, or how the prescribed treatment will affect the disease. Every day, the patient's blood is taken for analysis to measure the amount of the biomarker in the blood.

It must be emphasized that in order to use a surrogate endpoint for the purpose of control and supervision, the token must be validated or verified in advance. It is necessary to demonstrate that changes in the biomarker are correlated (consistent) with the clinical outcome in the case of a specific disease and the effect of treatment.

Additional sources

• World Health Organization (2008). Where are the patients in decision-making about their own care? Retrieved August 31, 2015

Problems of health and ecology

12. American Society of Echocardiography minimum standards for the cardiac sonographer: a position paper / S. M. Bierig // J Am Soc Echocardiogr. - 2006. - Vol. 19. - P. 471-474.

13. Antihypertensive drug therapy for mild to moderate hypertension during pregnancy / E. Abalos // The Cochrane Library Syst. Rev. - 2001. - Issue 4.

14. Antihypertensive drugs in pregnancy and fetal growth: evidence for “pharmacological programming” in the first trimester? / H. Bayliss // Hypertens Pregnancy. - 2002. - Vol. 21. - P. 161-174.

15. Antihypertensive therapy in the management of hypertension in pregnancy - a clinical double-blind study of pindolot / G. Bott-Kanner G. // Clin Exp Hypertension Pregnancy. - 1992. - Vol. 11. - P. 207-220.

16. Atenolol and fetal growth in pregnancies complicated by hypertension / C. Lydakis // Am. J. Hypertens. - 1999. - No. 12. - P. 541-547.

17. Australasian Society for the Study of Hypertension in Pregnancy: The detection, investigation and management of hypertension in pregnancy: full consensus statement / M. A. Brown // Am. J. Gynecol. - 2000. - Vol. 40. - P. 139-155.

18. Butters, L. Atenolol in essential hypertension during pregnancy / L. Butters, S. Kennedy, P. C. Rubin // Br. Med. J. - 1990. - Vol. 301.-P. 587-589.

19. Collins, R. Pharmacological prevention and treatment of hypertensive disorders in pregnancy / R. Collins, H.C. S. Wallenburg // Effective Care in Pregnancy and Childbirth / eds. I. Chalmers, M. Enkin, M. J. N. C. Keirse. - Oxford: Oxford University Press, 1989. - P. 512-533.

20. Effect of atenolol on birthweight / G. Y. Lip // Am. J. Cardiol. - 1997. - Vol. 79. - P. 1436-1438.

21. Effects of methyldopa on uteroplacental and fetal hemodynamics in pregnancy-induced hypertension / S. Montan // Am. J. Obstet. Gynecol. - 1993. - Vol. 168. - P. 152-156.

22. Fall in mean arterial pressure and fetal growth restriction in pregnancy hypertension: a meta-analysis / P. von Dadelszen // Lancet. - 2000. - Vol. 355. - P. 87-92.

23. Gallery, E.D.M. Antihypertensive treatment in pregnancy: analysis of different responses to oxprenolol and methyldopa /

E.D.M. Gallery, M. Ross, A. Z. Gyory // Br. Med. J. - 1985. - Vol. 291.-P. 563-566.

24. Gluckman, P. D. Maternal constraint of fetal growth and its consequences / P. D. Gluckman, M. A. Hanson // Semin Fetal Neonatal Med. - 2004. - Vol. 9, No. 5. - P. 419-425.

25. Guidelines Committee. 2003 European Society of Hypertension - European Society of Cardiology guidelines for the management of arterial hypertension // J. Hypertens. - 2003. - Vol. 21, No. 6. - P. 1011-1053.

26. Magee, L. A. Fortnightly review: management of hypertension in pregnancy / L. A. Magee, M. P. Ornstein, P. von Dadelszen // BMJ. - 1999. - Vol. 318, Issue 7194. - P. 1332-1336.

27. Magee, L. A. Oral beta-blockers for mild to moderate hypertension during pregnancy (Cochrane Review) / L. A. Magee, L. Duley // Cochrane Database Syst. Rev. - 2002. - Issue 1.

28. Preeclampsia - a state of sympathetic overactivity / H. P. Schobel // N. Engl. J. Med. - 1996. - Vol. 335. - P. 1480-1485.

29. Prevention of preeclampsia: a randomized trial of atenolol in hyperdynamic patients before onset of hypertension / T. R. Easterling // Obstet. Gynecol. - 1999. - Vol. 93. - P. 725-733.

30. Report of the National High Blood Pressure Education Program Working Group on High Blood Pressure in Pregnancy / R. W. Gifford // Am. J. Obstet. Gynecol. - 2000. - Vol. 183, No. 1. - P. 1-22.

31. The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension and of the European Society of Cardiology / G. Mancia // Eur. Heart J. - 2007. - Vol. 28. - P. 1462-1536.

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33. Use of antihypertensive medications in pregnancy and the risk of adverse perinatal outcomes: McMaster outcome study of hypertension in pregnancy 2 (MOS HIP 2) / J.G. Ray // BMC Pregnancy Childbirth. - 2001. - No. 1. - P.6.

34. World Health Organization - International Society of Hypertension 1999 Guidelines for the Management of Hypertension // High Blood Press. - 1999. - Vol. 8.-P. 1^3.

Received 29.10.2008

USE OF EVIDENCE-BASED MEDICINE DATA IN CLINICAL PRACTICE (message 3 - DIAGNOSTIC STUDIES)

A. A. Litvin2, A. L. Kalinin1, N. M. Trizna3

1Gomel State Medical University 2Gomel Regional Clinical Hospital 3Belarusian State Medical University, Minsk

An important aspect of evidence-based medicine is the completeness and accuracy of data presentation. The purpose of this article is to briefly review the principles of evidence-based medicine in research on the accuracy of diagnostic tests.

Diagnostic tests are used in medicine to establish the diagnosis, severity, and course of a disease. Diagnostic information is obtained from a variety of sources, including subjective, objective, special research methods. This article is based on a description of research quality measurement data, benefits various ways final statistics using the method of logistic regression and ROC analysis.

Keywords: evidence-based medicine, diagnostic tests, logistic regression, ROC analysis.

USE OF DATA OF EVIDENCE BASED MEDICINE IN CLINICAL PRACTICE (report 3 - DIAGNOSTIC TESTS)

A. A. Litvin2, A. L. Kalinin1, N. M. Trizna3

1Gomel State Medical University 2Gomel Regional Clinical Hospital 3Belarus State Medical University, Minsk

A prominent aspect of evidence based medicine is completeness and accuracy of data presentation. Article purpose is the short review of principles of evidence based medicine in the researches devoted to accuracy of diagnostic tests.

Problems of health and ecology

Diagnostic tests are used in medicine to screen for diagnosis, grade, and monitor the progression of disease. Diagnostic information is obtained from a multitude of sources, including sings, symptoms and special investigations. This article concentrates on the dimensions of study quality and the advantages of different summary statistics with logistic regression and ROC-analysis.

Key words: evidence based medicine, diagnostic tests, logistic regression, ROC-analysis.

When a doctor makes a judgment about the diagnosis based on the history and examination of the patient, he is rarely completely sure of it. In this regard, it is more appropriate to talk about the diagnosis in terms of its probability. It is still very common to express this probability not in the form of percentages, but with expressions such as "almost always", "usually", "sometimes", "rarely". Since different people invest different degrees of probability in the same terms, this leads to misunderstandings between doctors or between a doctor and a patient. Physicians should be as precise as possible in their conclusions and, if feasible, use quantitative methods.

Although the availability of such quantitative indicators would be very desirable, they are usually not available in clinical practice. Even experienced clinicians are often unable to accurately determine the likelihood of developing certain changes. There is a tendency to overdiagnose relative to rare diseases. It is especially difficult to quantify the probability, which can be very high or very low.

Since the establishment of reliable diagnostic criteria is the cornerstone clinical thinking, to develop statistical approaches to improve diagnostic prediction, accumulated clinical experience is used, which ideally should be presented in the form of computer data banks. In such studies, factors are usually identified

tori, which are in correlation with a particular diagnosis. These data can then be included in a multivariate analysis to determine which are significant independent predictors of the diagnosis. Some types of analysis allow you to identify important factors in predicting the diagnosis and then determine their "weight", which can be transformed into a probability in further mathematical calculations. On the other hand, the analysis allows us to identify a limited number of categories of patients, each of which has its own probability of having a particular diagnosis.

These quantitative approaches to diagnosis, often referred to as "prediction rules", are especially useful if they are presented in a user-friendly way and if their value has been extensively studied in a sufficient number and range of patients. For such prediction rules to be of real help to clinicians, they must be developed on representative patient populations using available reproducible tests so that the results obtained can be applied in medical practice everywhere.

In this regard, it is extremely important to be familiar with several of the most commonly used terms in research analysis and epidemiology, including prevalence, sensitivity, specificity, positive predictive value, and negative predictive value (Table 1) .

Table 1 - Systematic terms most commonly used in diagnostic tests

available absent

Positive a (true positive) b (false positive)

Negatives in (false negatives) r (true negatives)

Distribution (prior probability) = (a + c) / (a ​​+ b + c + d) = number of patients / total number of patients examined

Sensitivity \u003d a / (a ​​+ b) \u003d number of true positives / total number of patients

Specificity = r / (b+r) = number of true negatives / number of patients without the disease

False-negative rate = b / (a ​​+ b) = number of false-negative results / total number of patients

False positive rate = b / (b + d) = number of false positives / number of patients without the disease

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End of table 1

Test results Pathological condition

available absent

Positive predictive value = a / (a ​​+ b) = number of true positives / number of all positives

Negative predictive value = r / (c+r) = number of true negatives / number of all negatives

Total accuracy (accuracy) = (a+r) / (a+b+c+d) = number of true positives and true negatives / number of all results

Likelihood ratio of a positive test - = sensitivity / (1 - specificity)

likelihood ratio negative result test (likelihood ratio of a negative test) - = 1 - sensitivity / specificity

Questions answered by these characteristics of the diagnostic test:

1) sensitivity - how good is the test at detecting patients with the condition?

2) specificity - how good is the test at correctly excluding patients who do not have the condition?

3) the predictive value of a positive test result - if a person tests positive, what is the probability that he really has this disease?

4) the predictive value of a negative test result - if a person has a negative test, what is the probability that he really does not have this disease?

5) accuracy index - what proportion of all tests gave correct results (i.e. true positive and true negative results in relation to all)?

6) Likelihood ratio of a positive test - how much more likely is it that the test will be positive in a person with a disease compared to a healthy person?

Since only a minority of the prediction rules meet strict criteria such as the number and range of subjects examined and prospective validation of results, most of them are unsuitable for routine clinical use. Moreover, many prediction rules fail to assess the likelihood of every diagnosis or outcome faced by the clinician. A test with a certain sensitivity and specificity has different positive and negative predictive value when used in groups with different prevalence of the disease. The sensitivity and specificity of any test does not depend on the distribution

The severity of the disease (or the percentage of patients who have the disease out of all examined patients), they depend on the composition of the group of patients among whom this test was used.

In some situations, inaccurate knowledge of the sensitivity and specificity of the test in the studied patient population may limit its clinical value. Since the physician rarely knows (or may know) the patient population on which the test he or she prescribes has been standardized, the results obtained are much less reliable than is commonly thought. Moreover, for any diagnostic test, an increase in sensitivity will be accompanied by a decrease in specificity.

A model with high sensitivity often gives the true result in the presence of a positive outcome (detects positive examples). Conversely, a model with high specificity is more likely to give a true result in the presence of a negative outcome (finds negative examples). If we talk in terms of medicine - the problem of diagnosing a disease, where the model for classifying patients into sick and healthy is called a diagnostic test, then we get the following: 1) a sensitive diagnostic test manifests itself in overdiagnosis - the maximum prevention of missing patients; 2) a specific diagnostic test diagnoses only certain patients. Because no single quantity or derived measure can be expected to have both excellent sensitivity and specificity, it is often necessary to determine which measure is most valuable and necessary for decision making. Graphic image, called the ROC curve

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(Figure 1), linking the discussed characteristics of the test, shows the inevitability of a choice between striving for high sensitivity and specificity. Such a graphical representation indicates that the test results can be defined as normal or pathological, depending on whether

The disease is excluded if the test is highly specific or ruled out if the test is highly sensitive. Different tests may have different sensitivities and specificities. The sensitivity and specificity of more reliable tests are higher than those of invalid tests.

Figure 1 - Graphical representation of the internal discrepancy between sensitivity and specificity

ROC curve (Receiver Operator Characteristic) is the curve that is most commonly used to represent binary classification results in machine learning. The name comes from signal processing systems. Since there are two classes, one of them is called a class with positive outcomes, the second - with negative outcomes. The ROC curve shows the dependence of the number of correctly classified positive examples on the number of incorrectly classified negative examples. In the terminology of ROC analysis, the former are called true positive, the latter are called false negative sets. It is assumed that the classifier has some parameter, varying which we will get one or another breakdown into two classes. This parameter is often called the threshold, or cut-off value.

The ROC curve is obtained as follows. For each cutoff value, which varies from 0 to 1 in increments of, for example, 0.01, sensitivity values ​​Se and specificity Sp are calculated. Alternatively, the threshold may be each successive sample value in the sample. A dependency graph is built: sensitivity Se is plotted along the Y axis, 100% - Sp (one hundred percent minus specificity) is plotted along the X axis. As a result, a certain curve appears (Figure 1). The graph is often supplemented with a straight line y = x.

For an ideal classifier, the plot of the ROC curve passes through the upper left

the angle where the true positive rate is 100% or 1.0 (ideal sensitivity) and the false positive rate is zero. Therefore, the closer the curve is to the upper left corner, the higher the predictive power of the model. Conversely, the smaller the curvature of the curve and the closer it is to the diagonal line, the less efficient the model. The diagonal line corresponds to the "useless" classifier, i.e., the complete indistinguishability of the two classes.

When visually assessing ROC-curves, their location relative to each other indicates their comparative effectiveness. The curve located above and to the left indicates a greater predictive ability of the model. So, in Figure 2, two ROC curves are combined on one graph. It can be seen that model A is better.

Visual comparison of ROC curves does not always reveal the most efficient model. A peculiar method for comparing ROC curves is the estimation of the area under the curves. Theoretically, it changes from 0 to 1.0, but since the model is always characterized by a curve located above the positive diagonal, one usually speaks of changes from 0.5 (a "useless" classifier) ​​to 1.0 (an "ideal" model). This estimate can be obtained directly by calculating the area under the polyhedron bounded on the right and bottom by the coordinate axes and on the top left - by experimentally obtained points (Figure 3). The numerical indicator of the area under the curve is called AUC (Area Under Curve).

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Figure 2 - Comparison of ROC curves

Figure 3 - Area under the ROC curve

With large assumptions, we can assume that the larger the AUC, the better the predictive power of the model. However, you should be aware that the AUC indicator is intended rather for a comparative analysis of several models; AUC does not contain any

some information about the sensitivity and specificity of the model.

The literature sometimes provides the following expert scale for AUC values, which can be used to judge the quality of the model (table 2).

Table 2 - Expert scale of AUC values

AUC interval Model quality

0.9-1.0 Excellent

0.8-0.9 Very good

0.7-0.8 Good

0.6-0.7 Average

0.5-0.6 Unsatisfactory

The ideal model has 100% sensitivity and specificity. However, this cannot be achieved in practice; moreover, it is impossible to simultaneously increase the sensitivity and specificity of the model.

A compromise is found with the help of the cutoff threshold, since the threshold value affects the ratio of Se and Sp. We can talk about the problem of finding the optimal cut-off value (Figure 4) .

Figure 4 - “Balance point” between sensitivity and specificity

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The cutoff threshold is needed in order to apply the model in practice: to attribute new examples to one of two classes. To determine the optimal threshold, you need to set a criterion for its determination, because different tasks have their own optimal strategy. The criteria for choosing the cut-off threshold can be: 1) the requirement for a minimum value of sensitivity (specificity) of the model. For example, you need to ensure the sensitivity of the test is not less than 80%. In this case, the optimal threshold will be the maximum specificity (sensitivity), which is achieved at 80% (or a value close to

him "on the right" due to the discreteness of the series) sensitivity (specificity).

The given theoretical data are better perceived by examples from clinical practice. The first example we will focus on would be the diagnosis of infected necrotizing pancreatitis (data set taken from the database). The training sample contains 391 records with the selection of 12 independent variables in the following format (Table 3). Dependent variable (1 - presence of the disease, 0 - absence). The distribution of the dependent variable is as follows: 205 cases - no disease, 186 - its presence.

Table 3 - Independent variables for the diagnosis of infected pancreatic necrosis, logistic regression coefficients (example)

Independent variables Data format Coefficient, %

Number of days from onset > 14< 14 2,54

Number of days spent by patients on treatment in the ICU > 7< 7 2,87

Heart rate numerical value 1.76

Respiratory rate numerical value 1.42

Body temperature numerical value 1.47

Blood leukocytes numerical value 1.33

Leukocyte index of intoxication numerical value 1.76

Blood urea numerical value 1.23

Total plasma protein numerical value 1.43

Adequate antibiotic prophylaxis in establishing the diagnosis of severe acute pancreatitis yes / no -1.20

Performing minimally invasive medical and preventive operations yes / no -1.38

Presence of negative dynamics yes/no 2.37

Figure 4 depicts the resulting ROC which can be characterized as a very good curve. The predictive power of the model AUC = 0.839.

Figure 4 - ROC-curve of the diagnostic model of infected pancreatic necrosis

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Consider a fragment of the array of points “feeling of intra-abdominal pressure in patients with severe

validity-specificity” on the example of level acute pancreatitis.

Table 4 - Sensitivity and specificity of different levels of IAP for predicting the development of PPI (example)

IAP, mm Hg Art. Sensitivity, % Specificity, % Se + Sp Se - Sp

13,5 25 100 125 75

14,5 30 95 125 65

15,5 40 95 135 55

16,5 65 95 160 30

17,5 80 90 170 10

18,5 80 80 160 0

19,5 80 70 150 10

20,5 85 65 150 20

21,5 95 55 150 40

23,0 100 45 145 55

24,5 100 40 140 60

25,5 100 25 125 75

As can be seen from the table, the optimal threshold level of IAP in patients with acute destructive pancreatitis, which provides the maximum sensitivity and specificity of the test (or a minimum of type I and II errors), is 17.5 ± 2.3 (M ± SD) mm Hg, at which there is 80% sensitivity and 90% specificity of the method for determining the likelihood of developing infectious complications of pancreatic necrosis. The sensitivity is 80%, which means that 80% of patients with infected necrotizing pancreatitis have a positive diagnostic test. The specificity is 90%, so 90% of patients who do not have infected necrotizing pancreatitis have a negative test result. The balance point at which sensitivity and specificity approximately coincide - 80%, is 18.5. Overall, the positive predictive value of IAP measurement was 86%, and the negative predictive value was 88%.

Carrying out logistic regression and ROC analysis is possible using statistical packages. However, "Statistica" 6 and 7 (http://www.statistica.com) carry out this analysis only using the "Artificial Neural Networks" block. In SPSS (http://www. spss.com) (starting from version 13) ROC analysis is given only in the graphic module and one ROC curve is analyzed. SPSS displays the area under the curve (AUC), significance level, and sensitivity and specificity value at each measurement point. The optimal point (optimal cut-off) must be found by yourself from the table of sensitivity and 1-specificity. The MedCalc program will compare several ROC curves, mark the value of the variable in the table, when

which the ratio of sensitivity and specificity is optimal (optimal cut-off). SAS (http://www.sas.com), as well as R-Commander, has a curve comparison and point finding module, AUC. Logistic regression and ROC analysis are available from the free WINPEPI (PEPI-for-Windows) program (http://www.brixtonhealth.com/winpepi.zip) .

Conclusion

The art of diagnosis is constantly improving. New diagnostic tests appear daily, and the technology of existing methods changes. Overestimation of the accuracy of relevant studies, in particular as a result of bias due to poor research and publication practices, may lead to premature implementation of diagnostic tests and poor clinical decisions. Careful evaluation of diagnostic tests prior to their wide application not only reduces the risk of adverse outcomes due to misperceptions about the informativeness of the method, but can also limit the expenditure of health care resources by eliminating unnecessary examinations. An integral part of the evaluation of diagnostic tests are studies on the accuracy of diagnostic tests, the most informative of which are the method of logistic regression and ROC analysis.

REFERENCES

1. Greenhalch, T. Fundamentals of evidence-based medicine / T. Greenhalch; per. from English. - M.: GEOTAR-Media, 2006. - 240 p.

Problems of health and ecology

3. Vlasov, V. V. Introduction to evidence-based medicine / V. V. Vlasov. - M. MediaSphere, 2001. - 392 p.

4. Fletcher, R. Clinical epidemiology. Fundamentals of evidence-based medicine / R. Fletcher, S. Fletcher, E. Wagner; per. from English. - M.: MediaSphere, 1998. - 352 p.

5. Banerzhi, A. Medical statistics in plain language: an introductory course / A. Benerzhi; translation from English. - M.: Practical medicine, 2007. - 287 p.

6. Zhizhin, K. S. Medical statistics: textbook. allowance. - Rostov n / D .: Phoenix, 2007. - 160 p.

7. Deeks, J. J. Systematic reviews of evaluations of diagnostic and screening tests / J. J. Deeks // BMJ. - 2001. - Vol. 323. - P. 157-162.

8. Guidelines for meta-analyses evaluating diagnostic tests / L. Irwig // Ann. Intern. Med. - 1994. - Vol. 120. - P. 667-676.

9. Systematic reviews and meta-analysis for the surgeon scientist /

S. S. Mahid // Br. J. Surg. - 2006. - Vol. 93. - P. 1315-1324.

10. Meta-analytical methods for diagnostic test accuracy / L. Irwig // J. Clin. epidemiol. - 1995. - Vol. 48. - P. 119-130.

11. Users" guides to the medical literature. How to use an article about a diagnostic test. A. Are the results of the study valid? / R. Jaeschke // JAMA. - 1994. - Vol. 271. - P. 389 -391.

12. Use of methodological standards in diagnostic test research: getting better but still not good / M. C. Read // JAMA. - 1995. - Vol. 274.-P. 645-651.

13. StAR: a simple tool for the statistical comparison of ROC curves / I. E. Vergara // BMC Bioinformatics. - 2008. - Vol. 9. - P. 265-270.

14. A comparison of parametric and nonparametric approaches to ROC-analysis of quantitative diagnostic tests / K. O. Hajian-Tilaki // Medical Decision Making. - 1997. - Vol. 17, N. 1. - P. 94-102.

15. Receiver operator characteristic (ROC) curves and nonnormal data: An empirical study / M.J. Goddard // Statistics in Medicine. - 1989. - Vol. 9, N. 3. - P. 325-337.

16. Possibilities of predicting infected pancreatic necrosis / A. A. Litvin [et al.] // Problems of health and ecology. - 2007. - T. 12, No. 2. - S. 7-14.

17. Method for monitoring intra-abdominal pressure in patients with severe acute pancreatitis / A. A. Litvin [et al.] // Problems of health and ecology. - 2008. - T. 16, No. 2. - S. 80-85.

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19. Zhu, X. A short preview of free statistical software packages for teaching statistics to industrial technology majors / X. Zxu // J. Ind. technology. - 2005. - Vol. 21, N. 2. - P. 10-20.

20. Borovikov, V. STATISTICA: the art of computer data analysis. For professionals / V. Borovikov. - St. Petersburg: Peter, 2001. - 656 p.

21. Buyul, A. SPSS: the art of information processing. Analysis of statistical data and restoration of hidden patterns / A. Byuyul. - St. Petersburg: DiaSoftYUP, 2002. - 608 p.

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Received 24.10.2008

UDC 616.1:616-009.12:616-005.8:616.831-005.1

SOME INDICATORS OF MICROCIRCULATION AND ENDOTHELIAL DAMAGE IN ASSESSING THE RISK OF DEVELOPMENT OF STROKE, MYOCARDIAL INFARCTIONS, FATAL OUTCOMES IN PATIENTS WITH ARTERIAL HYPERTENSION

V. I. Kozlovsky, A. V. Akulyonok Vitebsk State Medical University

Purpose of the study: to identify factors associated with an increased risk of myocardial infarction, cerebral stroke, and death in patients with stage II arterial hypertension (AH).

Material and Methods: The study included 220 patients with II degree AH (mean age 57 ± 8.4 years) who were hospitalized due to hypertensive crisis, and 30 people without hypertension (mean age

53.7 ± 9 years).

Results: 29 strokes, 18 myocardial infarctions, 26 deaths were recorded in the group of patients with II degree AH during 3.3 ± 1 years of follow-up. An increase in the number of circulating endothelial cells (ECC), aggregation of leukocytes, platelets, and adhesion of leukocytes in hypertensive patients was associated with an increased risk of myocardial infarction, stroke, and death.

Conclusion: indicators of the number of CECs, aggregation of platelets and leukocytes, and leukocyte adhesion can be used to identify groups of hypertensive patients at an increased risk of developing myocardial infarctions, strokes and deaths, as well as to create complex prognosis models.

Key words: arterial hypertension, risk, myocardial infarction, stroke, death, circulating endotheliocytes.

SOME FINDINGS OF MICROCIRCULATION AND ENDOTHELIAL DAMAGE IN ESTIMATION OF RISK FOR STROKES, MYOCARDIAL INFARCTIONS, LETHAL OUTCOMES IN HYPERTENSIVE PATIENTS

V. I. ^zlovsky, A. V. Akulionak Vitebsk Statel Medical University

Objective: to determine factors associated with increased risk for development of strokes, myocardial infarctions, lethal outcomes in patients with arterial hypertension (AH) II degree.

Methods: 220 patients with AH II degree (mean age 57 ± 8.4 years), complicated by hypertensive crisis, and 30 persons without AH (mean age 53.7 ± 9 years) were followed-up for 3.3±1 years .

Results: elevation of number of circulating endothelial cells (CEC), aggregation of platelets and leukocytes, adhesion of leukocytes in hypertensive patients were associated with increased risk for development of strokes, myocardial infarctions, lethal outcomes.

Evidence-based medicine sources are constantly growing, but despite this, the Cochrane Library remains the most important. It was created as part of the Cochrane collaboration of an international community of scientists in various specialties, who set out to find, systematize and summarize the results of all randomized controlled clinical trials ever conducted. The principal criterion for the selection of studies is the selection of subjects by random sampling. The Cochrane Library contains information on all trials conducted on this topic (trials of drugs, treatments, etc.), as well as systematic reviews on the most relevant and controversial topics in medicine, which are regularly updated. The data in the Cochrane Library are in electronic format and can be accessed online or distributed on laserdisc by subscription.

Having access to the Cochrane Library, a doctor or researcher can quickly check whether the information contained in the analyzed scientific article corresponds to accepted world experience, whether similar studies have been conducted before and what results have been obtained.

It is important that in recent years not only databases have appeared where you can find almost any medical information of interest, but also unified standards for reporting the results of randomized controlled trials (CONSORT) have been developed, the purpose of which is to improve the quality of reports on randomized controlled trials (RCTs).

For a comprehensive assessment of the results of an RCT, it is necessary to have a good understanding of the features of its structure, conduct, data analysis and interpretation. The rapid growth in the number of RCTs has made the question of the need to correct

presentation of their results, as very often the quality of the reports remains unsatisfactory. A group of researchers and medical journal editors developed the CONSORT - CONsolidated Standards Of Reporting Trials to help researchers improve the quality of reports by using a specific algorithm that reflects the process of conducting and analyzing RCTs. If its authors submit incomplete or incorrectly prepared reports, this makes it extremely difficult or often impossible to interpret the data obtained. Very often, tendentiously presented results are the basis of unscrupulous practice of scientific publications, when erroneous results are presented as some new truth.

The standard sections of the report are the following sections: title, abstract, "Introduction", "Research methods", "Results" and "Discussion". It should contain detailed information about the relevance and objectives of the study, the features of its structure, conduct and analysis of data. For example, insufficient information about randomization leads to an incorrect assessment of the effectiveness of the intervention. In order to judge the strengths and weaknesses of RCTs, the reader needs to be aware of the quality of the methods used.

In addition to the design of the study, a detailed scheme of its conduct is needed, which should reflect the change in the composition of its participants over time (inclusion of participants, randomization for the appointment of one or another intervention, observation and data analysis). These data provide a clear indication of how many patients in each group were included in the primary analysis and to conclude whether the RCT used data analysis based on the assumption that all patients received the prescribed treatment.

The CONSORT standards were first published in the mid-1990s by a group of authors that included clinical researchers (physicians), specialists in medical statistics and noninfectious epidemiology, and editors of leading biomedical journals. These standards have become the basis for editorial requirements when preparing articles for publication in medical journals. CONSORT standards periodically

are updated and latest version available on the Internet: www. consort-statement.org

The use of CONSORT standards really contributes to improving the quality of RCT reports and medical publications, reducing the number of unsatisfactory medical publications from 61% to 39%.

Currently, CONSORT standards strongly recommend indicating whether the ethics committee of the institution where the study was conducted has been approved; sources of funding and registration number of the study, such as the International Standard RCT number (International Standard Randomized Controlled Trial Number - ISRCTN), assigned before the start of the study.

Today, CONSORTs include a 22-item checklist (Table 8.1) and an RCT design (Figure 8.1) that focus primarily on improving the quality of reporting of simple two-arm RCTs. However, the CONSORT principles allow them to be used in the preparation of study reports with any other design.

CONSORT standards apply not only to RCT participants, but also to any researchers, since they are widely used by reviewers and editors of medical journals when selecting articles for publication in order to eliminate systematic errors that could lead to erroneous results and conclusions. Particular attention is paid to the statistical presentation of the results of a clinical trial in accordance with the provisions of the Uniform Requirements for Manuscripts Submitted to Biomedical Journals.

CONSORT was initially based on the principles of evidence-based medicine, previously used in the development of standards for the presentation of meta-analyses of randomized trials, meta-analyses of observational studies and research materials that evaluated the effectiveness of diagnostic methods.

Currently, in addition to the Cochrane Library, there are about 200 databases where you can find materials that comply with the principles of evidence-based medicine (the most important of these are listed in Table 8.2).

Rice. 8.1. Design of a randomized trial that reflects data on the number of participants at all stages (inclusion of participants, randomized assignment of one or another intervention, observation and data analysis)

Table 8.1. Checklist of sections to include in a randomized trial report

Continuation of table 8.1

End of table 8.1

Table 8.2. Medical electronic databases that use evidence-based medicine data

For the development of new and analysis of existing clinical guidelines, the following Internet resources may be useful.

National Guidelines Clearinghouse (NGC), www. guideline.gov

Guide preventive measures in Clinical Practice (Guide to Clinical Preventive Service), http://cpmcnet. columbia.edu/texts/gcps

Guide to Clinical Preventive Service, Second Edition, http://odphp.osophs.dhhs.gov/pubs/guidecps/default.htm

Family Practice Disease Treatment Guides, www.familymed.com/References/ReferencesFrame.htm

Institute for Clinical Systems Improvement, www.icsi.org

Agency for Health Research and Care Quality: Put Prevention into Practice (AHRQ), www.ahcrp.gov/clinic/ppipix.htm

Agency for Healthcare Quality and Research (AHRQ), www.ahrq.gov

AIHA Multilingual Library, www.eurasiahealth. org/english/library/index.cfm www.eurasiahealth.org/russian/library/index.cfm

Agency for Health Research and Care Quality: Put Prevention into Practice (AHRQ), www.ahcrp.gov/clinic/ppipix.htm

Canadian Medical Association: Guidelines for Canadian Clinical Practice Guidelines, www.cma. ca/cpgs/gsspg-e.htm

Healthcare on scientific basis(Bandolier: Evidence-Based Health Care), www.jr2.ox.ac.uk/bandolier/index.html

Search for existing clinical practical guides can be done at:

http://www.guideline.gov (US National Guideline Clearinghouse);

Http://www.phppo.cdc.gov/CDCRecommends/AdvSearchV.asp (Center for Disease Control and Prevention, USA);

http://www.ahrg.gov/clinic/cpgsix.htm (Agency for Healthcare Research

and Quality, USA);

Http://hstat.nlm.nih.gov (Health Services Technology Assessment Text and National Library of Medicine, USA);

Http://mdm.ca/cpgsnew/cpgs/index.asp (Canadian Medical Association Infobase of Clinical Practice Guidelines);

Http://www.hc-sc.gc.ca/pphb-dgspsp/dpge.html (Health Canada - Population and Public Health Branch Guidelines);

Http://www.nice.org.uk (National Institute for Clinical Excellence - NICE, UK);

http://www.eguidelines.co.uk (eGuidelines, UK);

Http://www.shef.ac.uk/seek/guidelines.htm (Sheffield Evidence for Effectiveness and Knowledge Clinical Guidelines, UK);

Http://www.nelh.nhs.uk/guidelinesfinder (National Electronic Library

for Health, UK);

Http://www.prodigy.nhs.uk/ClinicalGuidance (PRODIGY Clinical Guidance, UK);

Http://www.sign.ac.uk (Scottish Intercollegiate Guidelines Network, Scotland);

Http://www.lehtinen.de/english/english/view (German Guidelines Information Service, Germany);

http://www.health.gov.au/hfs/nhmrc/publicat/cp-home.htm(Australian National Health and Medical Research Council, Australia);

Http://www.nzgg.org.nz/library.cfm (New Zealand Guidelines Group, New Zealand).

Useful information on various aspects of evidence-based medicine in relation to more “narrow” specialties can be found at:

American Holistic Health Association, www.ahha.org;

American Whole Health and Rebus Inc., www.WholeHealth.com;

European Society of Cardiology, www.escardio.org;

Vascular Disease Foundation, www.vdf.org;

British Dental Association, www.bda-dentistry.org.uk;

British Dental Health Foundation, www.dentalhealth.org.uk;

The American Society for Bone and Mineral Research, www.asbmr.org;

The Thyroid Society, www.the-thyroid-society.org;

American Academy of Family Physicians, www.aafp.org;

Canadian Health Network, www.canadian-health-network.ca;

Organization Mondiale de Gastro-Enterology, www.omge.org;

British Liver Trust, www.britishlivertrust.org.uk;

Society of Gynecologic Oncologists, www.sgo.org;

American Cancer Society, www.cancer.org;

International Society for Infectious Diseases, www.isid.org;

Hepatitis B Foundation, www.hepb.org;

American Society of Gene Therapy, www.asgt.org;

Human Genome Project Information, www.ornl.gov/hgmis;

American Academy of Neurology, www.aan.com;

Alzheimer's Disease International, www.alz.co.uk;

International Federation of Gynecology and Obstetrics, www.figo.org;

OBGYN.net, www.obgyn.net;

International Society of Refractive Surgery, www.isrs.org;

The Glaucoma Foundation, www.glaucoma-foundation.org;

American Academy of Orthopedic Surgeons, www.aaos.org;

National Osteoporosis Society, www.nos.org.uk;

American Academy of Pediatrics, www.aap.org;

KidsGrowth.com, www.kidsgrowth.com;

World Psychiatric Association - WPA online, www.wplanet.org;

The Mental Health Foundation, www.mentalhealth.org.uk;

Global Initiative for Asthma, www.ginasthma.com;

Canadian Lung Association, www.lung.ca;

International League of Associations for Rheumatology, www.ilar.org;

Arthritis Foundation, www.arthritis.org;

International Society of Surgery, www.iss-sic.ch;

The International Radiosurgery Support Association, www.irsa.org;

The Bristol Urological Institute, www.bui.ac.uk;

American Foundation for Urologic Disease, www.impotence.org;

The American Academy of Forensic Science, www.aafs.org;

The American Medical Association, www.ama-assn.org;

The Antibody Resource Page, www.antibodyresource.com;

Site for different medical specialties, www.atemergency.com;

Drug directory publication MIMS, www.atmedican-asia.com;

British Medical Journal, www.bmj.com;

Neurology, Gastroenterology (journals), www.b2imed.com;

Site for information related to clinical trials, www.centerwatch.com;

Interactive online conferences, www.cyberounds.com;

Dental-related Internet resources, www.dental-resources.com;

The CPR Diary and Patient Education, www.edotmd.com;

Exercise Research Associates, www.exra.org;

Pediatrics health problems, www.generalpediatrics.com;

The Global Drug Database, www.globaldrugdatabase.com;

Electronic Journal for hypertension, dialysis and clinical nephrology, www.hdcn.com;

General Information portal on health and medicine, www.healthscount.com;

Internet version of a leading consumer health magazine in the Asia Pacific, www.healthtoday.net;

The Howard Hughes Medical Institute, www.hhmi.org;

Medical database, www.internets.com/mednets;

The Institute for the Study and Treatment of Pain, www.istop.org, www. lipitor.com;

SmartMed site for physicians, www.medicinenet.com;

Medical conferences worldwide, www.mediconf.com;

Site specialties: clinical management series, ask the experts, conference, and lost more, www.medscape.com;

Free internet service, medical information in a concise slide presentation format, www.medslides.com;

Microbiology, virology sites, www.microbiol.org;

Nephrology, www.nephroworld.com;

Neuroscience resources, www.neuroguide.com;

The United States National Library of Medicine, www.nlm.nih.gov, www.norvasc.com;

Denmarks Lundbeck Institute, www.psychiatrylink.com;

Radiology related sites, www.radcenter.com;

Post-graduate medical life, www.residentpage.com;

Reuters Health offers, www.reutershealth.com;

Holistic health and alternative medicine, www.saffronsoul.com;

Society of Critical Care Medicine, www.sccm.org, www.telemedicine.org;

Site for cardiologists, www.theheart.org;

Transplantation and donation, www.transweb.org;

Urology, www.uroguide.com;

Surgical education, www.vesalius.com;

The Virtual Hospital, www.vh.org;

The Directory of Dentists, www.webdental.com;

Web Health Centre, www.webhealthcentre.com.

Professional Associations Alternative Medicine

National Integrative Medicine Council, www.nimc.org;

The British Naturopathic Association, www.naturopaths.org.uk;

The Canadian Complementary Medical Association, www.ccmadoctors.ca;

Association of European Pediatric Cardiologists, www.aepc.org;

Cardiac Society of Australia and New Zealand, www.csanz.edu.au;

Society of Geriatric Cardiology, www.sgcard.org;

American Dental Association, www.ada.org, Dentalxchange.com;

European Union of Dentists, www.europeandentists.org;

American Academy of Dermatology, www.aad.org, DermWeb.com, www.dermweb.com.

Diabetes & Endocrinology

American Thyroid Association, www.thyroid.org;

Association of British Clinical Diabetologists, www.diabetologistsabcd.org.uk;

Society for Endocrinology, www.endocrinology.org;

Pacific Dermatologic Association, www.pacificderm.org.

family medicine

Primary Care Doctors" Organization Malaysia, www.jaring.my/pcdom;

Royal Australian College of General Practitioners, www.racgp.org.au;

Royal College of General Practitioners, www.rcgp.org.uk.

gastroenterology

British Society of Gastroenterology, www.bsg.org.uk, GastroHep.com;

Philippine Society of Gastroenterology, www.psgpsde.com.ph.

Hematology - Oncology

American Society of Clinical Oncology, www.asco.org;

American Society of Hematology, www.hematoIogy.org, Bonetumor.org.

Infectious Diseases

Infectious Diseases Society of America, www.idsociety.org;

International Association of Physicians in AIDS Care www.iapac.org;

National Foundation for Infectious Diseases, www.nfid.org.

Molecular Medicine

BioMetNet, www.bmn.com;

Gene Therapy - A Professional Community, www.gtherapy.co.uk;

National Society of Genetic Counsellors, www.nsgc.org.

Neurology

American Association of Electrodiagnostic Medicine, www.aaem.net;

American Board of Psychiatry and Neurology, www.abpn.com;

National Neurotrauma Society, www.edc.gsph.pitt.edu/neurotrauma.

Obstetrics & Gynecology

Association of Professors of Gynecology and Obstetrics, www.apgo.org;

European Society of Human Reproduction and Embryology, www. esre.com;

International Society of Gynecologic Endoscopy, www.isge.org.

Ophthalmology

American Board of Ophthalmology, www.abop.org;

American Society of Cataract and Refractive Surgery, www.ascrs.org;

Contact Lens Association of Ophthalmologists, www.clao.org.

Orthopedics

American Association of Orthopedic Surgeons, www.aaos.org;

Asia Pacific Orthopedic Association, www.sapmea.asn.au/apoaold.htm;

Clinical Orthopedic Society, www.cosociety.org.

Pediatrics

European Society for Pediatric Urology, www.espu.org;

Society for Pediatric Anesthesia, www.pedsanesthesia.org;

Society for Pediatric Radiology, www.pedrad.org.

Psychiatry

American Psychiatric Association, www.psych.org;

Canadian Psychiatric Association, www.cpa-apc.org;

Society of Clinical Psychiatrists, www.scpnet.com.

Respiratory care

American Association of Respiratory Care, www.aarc.org;

National Association for Medical Direction of Respiratory Care, www. namdrc.org;

The Canadian Society of Respiratory Therapists, www.csrt.com.

Rheumatology

Association of Rheumatology Health Professionals, www.rheumatology. org/arhp;

British Society for Rheumatology, www.rheumatology.org.uk;

New Zealand Rheumatology Association, www.rheumatology.org.nz.

Canadian Society of Plastic Surgeons, www.plasticsurgery.ca;

International Society for Minimally Invasive Cardiac Surgery, www. ismics.org;

Society of Thoracic Surgeons, www.sts.org. Urology

American Urological Association, www.auanet.org;

European Association of Urology, www.uroweb.org;

International Society of Andrology, www.andrology.org.

Orthopedics

Orthopedic Network, www.orthonetwork.cog.

Public Info Sites

Alternative Medicine, Holistic.com;

Cardiology HeartPoint, www.heartpoint.com;

Dentistry, Smileworks.com;

Dermatology, OneSkin.com;

Diabetes & Endocrinology, EndocrineWeb.com;

Family Medicine, MayoClinic.com www.mayohealth.org;

Gastroenterology Gastronet, www.gastro.net.au;

Haematology - Oncology CancerSource.com, www.cancersource. com/community;

Infectious Diseases Infection Ctrl Online, www.infectionctrl-online. com;

Molecular Medicine The DNA Files, www.dnafiles.org;

Neurology Gateway to Neurology, http://neuro-www.mgh.harvard.edu;

Obstetrics & Gynecology Oestronaut, www.womenshealth.org;

Ophthalmology National Eye Institute, www.nei.nih.gov;

Pediatrics QualKids, www.qualkids.com;

Psychiatry Depression Alliance, www.depressionalliance.org;

Respiratory Care The Breathing Space, www.thebreathingspace.com;

Rheumatology Arthritis Link, www.arthritislink.com;

Surgery, Transplantation.org;

Urology UrologyChannel, www.urologychannel.com.

CME Providers

American Academy of Physical Medicine & Rehabilitation, www. aapmr.org/cme.htm;

American College of Emergency Physicians, www.pain.acep.org/acep;

ArcMesa Educators, www.arcmesa.com/cont_ed/profhome.jhtml7P_ ID=9;

Baylor College of Medicine Online CME, www.baylorcme.org, BreastCancerEd.net;

Cancer Control: Journal of the Moffitt Cancer Centre, www.moffitt.usf. edu/providers/ccj;

CardioVillage.com;

Cleveland Clinic Center for Continuing Education www. clevelandclinicmeded.com/online/topics.htm, Cme.cybersessions.org, CMEacademy.com, CMECourses www.cmecourses.com;

Emerging Issues in Neurotoxin Therapy, www.neurotoxinonline.com;

Geriatric Times, www.medinfosource.com/gtycme.html;

Hospital Practice, www.hosppract.com/cme.htm;

Interactive Grand Rounds, http://igr.medsite.com;

Physician and Sports Medicine Online, www.physsportsmed.com;

Physician Assistant Journal, www.pajournal.com/pajournal/cme/ce.html;

Postgraduate Medicine CME Online, www.postgradmed.com/cme.htm;

Power-Cancer C.E., www.powerpak.com/CE;

Pragmaton Office of Medical Education, www.pome.org;

Psychiatric Times, www.mhsource.com/pt/cme.html;

Southern Medical Association On-Line, www.sma.org;

Stanford Radiology Online CME, http://radiologycme.stanford.edu/online;

The Journal of Clinical Psychiatry, www.psychiatrist.com/cmehome;

The Pediatric Pharmacy Advocacy Group, www.cecity.com/ppag/index.htm;

U.S. Pharmacist, www.uspharmacist.com;

University of Alabama School of Medicine, http://main.uab.edu/uasom/new/show.asp?durki=14510;

Vaccine Safety, www.vaccinetoday.com/aap.htm;

Virtual Dermatology, http://erl.pathology.iupui.edu/cases/dermcases/. dermcases.cfm

Virtual Lecture Hall, www.vlh.com.

Literature

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2. Altman D.G. The scandal of poor medical research. BMJ 1994; 308:283-4.

3. Bailor J.C. 3rd, Mosteller F. Guidelines for statistical reporting in articles for medical journals. Amplifications and explanations. Ann Intern Med 1988;

4. Chalmers I. Current controlled trials: an opportunity to help improve the quality of clinical research. Ssht Control Trials Cardiovasc Med 2000: 3-8.

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cluster randomized trials: for discussion. Stat Med 2001; 20:489-96.

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This article will help you take a more realistic look at the results of medical research that we often use in writing our articles, as well as better navigate the flow of advertising information that constantly tries to mislead us by appealing to "scientifically proven" results.

"There are three kinds of lies: lies, damned lies and statistics"
Benjamin Disraeli, British Prime Minister

On the pages of our articles and especially on the forum, we often appeal to evidence-based medicine. What is evidence-based medicine?

evidence-based medicine(Eng. Evidence-based medicine - medicine based on evidence) - the term describes an approach to medical practice in which decisions on the use of preventive, diagnostic and therapeutic measures are taken based on the evidence obtained for their effectiveness and safety, and involving the search, comparison, generalization and wide dissemination of the evidence obtained for use in the interests of patients.

evidence-based medicine is a set of methodological approaches to conducting clinical trials, evaluating and applying their results. In a narrow sense, “evidence-based medicine” is a method (variant) of medical practice, when a doctor uses in the management of a patient only those methods whose usefulness has been proven in benign studies.

To simplify it completely, we can say that evidence-based medicine is medicine based on methods whose effectiveness has been proven. The methodological basis of evidence-based medicine is clinical epidemiology - a science that develops clinical research methods that make it possible to draw scientifically based conclusions, minimizing the impact of systematic and random errors on the results of the study. And here comes the most main question What is the criterion for benign research? We will talk about some signs of benign research in this article.

The main tool of clinical epidemiology is statistics. Statistics is a science that studies methods of systematic observation of mass phenomena of human social life, compiling their numerical descriptions and scientific processing of these descriptions. It is with the help of biomedical statistics that all the results of any biological and medical research are described and presented to the reader in the form of numbers, tables, graphs, histograms. And here the main thing is not to fall under the charm of numbers.

Control group quality

If we are talking about percentages, which are often used to describe results, because. they are very indicative, you need to clearly understand what is the starting point, i.e. which is taken as 0%. That is, when you are told "20% higher", you immediately ask "compared to what?". If this is a study of some kind of drug (drug, cosmetic), then you need to know that the control groups that did not take this drug at all are a thing of the past. The study must be conducted using a placebo. Placebo is a physiologically inert substance used as a drug, the positive therapeutic effect of which is associated with the unconscious psychological expectation of the patient. A placebo cannot act directly on the conditions for which the drug is being investigated. In addition, the term "placebo effect" refers to the very phenomenon of non-drug effects, not only the drug, but, for example, radiation (sometimes different "flashing" devices, "laser therapy", etc. are used). Lactose is often used as a placebo substance. The degree of manifestation of the placebo effect depends on the suggestibility of the person and the external circumstances of the "treatment", for example, on the size and bright color of the pill, the degree of confidence in the doctor, the authority of the clinic. And of course, studies that compare an investigational drug with its predecessor or similar competitors cannot be taken seriously.

Research Evidence

It is also important to find out what kind of study the study belongs to, which can be learned from the structure of this work. Each species has its own evidentiary weight, according to which it is possible to compile a hierarchy of their evidence (listed in ascending order of evidence):
1) description of individual cases;
2) description of the series of cases;
3) a retrospective case-control study;
4) analytical one-time study;
5) prospective cohort (population) study;
6) randomized controlled trial medical interventions(methods of treatment, prevention);
7) meta-analysis - summarizing the results of several randomized clinical trials.

Let's give brief description different types of study structure.

Description of individual cases- the oldest method of medical research. It consists of describing a rare observation, a "classic" case ("classical" cases, by the way, are never frequent), or a new phenomenon. Scientific hypotheses in such a study are not put forward and are not tested. However, this method of research is also important in medicine, since the description of rare cases or phenomena cannot be underestimated.

Description of the case series- a study that usually includes descriptive statistics of a group of patients selected for some reason. Descriptive studies are used, for example, in epidemiology to study the influence of uncontrolled factors on the occurrence of a disease.

Case-control study- a retrospective study in which, according to archival data or a survey of its participants, groups of these participants (patients) with and without a certain disease are formed, and then the frequency of exposure to a suspected risk factor or cause of the disease is retrospectively assessed. Such studies are more likely to advance scientific hypotheses rather than test them. The advantage of this type of research is its relative simplicity, low cost and speed of implementation. However, case-control studies are fraught with many potential biases. The most significant of them can be considered systematic errors associated with the selection of study participants, and a systematic error that occurs during measurement.

Single-stage (cross-sectional) study- a descriptive study that includes single surveyed groups of participants and conducted to assess the prevalence of a particular outcome, the course of the disease, as well as the effectiveness of diagnosis. Such studies are relatively simple and inexpensive. The main problem is the difficulty of forming a sample that adequately reflects the typical situation in the studied population of patients (representative sample).

Prospective (cohort, longitudinal) study- a study in which a selected cohort of participants is observed for a certain period of time. First, a cohort (or two cohorts, such as those exposed to the risk factor and those not exposed to it) is identified, and then it (them) is monitored and data is collected. This is in contrast to a retrospective study in which cohorts are isolated after data is collected. This type of research is used to identify risk factors, prognostic factors, causes of diseases, to determine the incidence rate. Prospective studies are very laborious, as they must be carried out for a long time, cohorts must be large enough due to the fact that detected events (for example, the occurrence of new cases of the disease) are quite rare.
The main problems that arise when conducting a prospective study are as follows:
- the probability of the events studied depends on the method of sampling (cohorts; for example, observed participants from a risk group are more likely to get sick than participants from an unorganized population);
- when participants drop out during the course of the study, it is necessary to find out if this is not related to the outcome or factor being studied;
- over time, the strength and nature of the impact of the studied factor may change (for example, the intensity of smoking as a risk factor for the development of coronary disease

randomized trial- this is a dynamic study of any preventive, diagnostic or therapeutic effect, in which groups are formed by random distribution of research objects into groups (randomization). The most well-known variant of a randomized trial is a clinical trial. A clinical trial is a prospective comparative study of the effectiveness of two or more interventions (curative, preventive) or diagnostic method, in which groups of subjects are formed using randomization, taking into account inclusion and exclusion criteria. In this case, there is usually a hypothesis that arose before the study regarding the effectiveness of the tested methods, which is verified during the test.

Meta-analysis- quantitative analysis of the combined results of several clinical trials of the same intervention in the same disease. This approach provides greater statistical sensitivity (power) than in any single study by increasing the sample size. Meta-analysis is used to summarize the results of many trials, often contradictory.

Clinical Efficiency

When reading scientific and medical articles, you need to understand for yourself which characteristics were measured during the study - clinical or biological (biochemical, physiological, genetic, etc.). Here's one small example on the study of the use of halothane and morphine in open heart surgery.

Halothane is a drug widely used in general anesthesia. It is strong, easy to use and very reliable. Halothane is a gas that can be administered through a respirator. Entering the body through the lungs, halothane acts quickly and briefly, therefore, by adjusting the supply of the drug, anesthesia can be quickly controlled. However, halothane has a significant drawback - it inhibits myocardial contractility.

The study included patients who had no contraindications to either halothane or morphine. The mode of anesthesia (halothane or morphine) was chosen at random.

The study included 122 patients. Half of the patients used halothane (Group 1), half - morphine (Group 2). On average, in patients treated with halothane, the minimum blood pressure was 6.3 mm Hg. Art. lower than in patients treated with morphine. The spread of values ​​is quite large, and the ranges of values ​​overlap a lot. The standard deviation in the halothane group was 12.2 mmHg. Art. in the morphine group - 14.4 mm Hg. Art. Statistical analysis showed that the difference is statistically significant, so it can be concluded that morphine reduces blood pressure to a lesser extent than halothane.

As you remember, Conahan et al. proceeded from the assumption that morphine depresses blood circulation to a lesser extent than halothane and therefore is preferable for general anesthesia. Indeed, blood pressure and cardiac index were higher with morphine than with halothane, and these differences were statistically significant. However, it is too early to draw conclusions, because differences in operational mortality have not yet been analyzed, and this indicator is the most significant from a practical point of view.

So, among those who received halothane (Group 1), 8 patients out of 61 (13.1%) died, and among those who received morphine (Group 2), 10 out of 67 (14.9%) patients died. The difference is 1.8%. Statistical analysis showed that the difference was not statistically significant. Therefore, although halothane and morphine act differently on the circulation, there is no reason to speak of a difference in operative lethality. Essentially, one can say that clinical effects the two drugs are not different.

This example is very instructive: we have seen how important it is to take into account the outcome of the current. The body is complex, the action of any drug is diverse. If the drug has a positive effect on the cardiovascular system, then it is possible that it negatively affects, for example, the respiratory system. Which of the effects will outweigh and how it will affect the final result is difficult to predict. That is why the effect of a drug on any indicator, whether it be blood pressure or cardiac index, cannot be considered evidence of its effectiveness until clinical effectiveness has been proven. In other words, one should clearly distinguish between process indicators - all kinds of changes in biochemical, physiological and other parameters that we believe play a positive or negative role - and outcome indicators that have real clinical significance. Thus, changes in blood pressure and cardiac index under the influence of halothane and morphine are process indicators that did not affect the result indicator - operational lethality. If we were content with observing process indicators, we would conclude that morphine is better than halothane, although, as it turned out, the choice of anesthetic does not affect mortality at all.

When reading medical publications or listening to the arguments of a supporter of a particular treatment method, one should first of all understand what indicators are being discussed - the process or the result. Demonstrating the impact of some factor on the process is much easier than finding out whether it affects the result. Recording process indicators is usually simple and does not take much time. On the contrary, finding out the result, as a rule, requires painstaking long-term work and is often associated with subjective measurement problems, especially when it comes to quality of life. And yet, when deciding whether the proposed method of treatment is necessary, you need to make sure that it has a positive effect on the outcome indicators. Believe me, the patient and his family are primarily concerned with the result, not the process.

References

1. Evidence Based Medicine Working Group, 1993
2. Vlasov V.V., Semernin E.N., Miroshenkov P.V. Evidence-based medicine and principles of methodology. World of Medicine, 2001, N11-12.
3. Rebrova O.Yu. Statistical analysis of medical data. Application of the application package STATISTICA. Moscow: "MediaSphere", 2002.
4. Glanz S. Medico-biological statistics. Per. from English. - Moscow: "Practice", 1998.

There are several definitions of evidence-based medicine:

• This new technology collection, analysis, synthesis and use of medical information to make optimal clinical decisions.
• It is the conscious, clear and unbiased use of the best available evidence to guide decisions about care for individual patients.
• It is the enhancement of the clinician's traditional skills in diagnosis, treatment, prevention, and other areas through the systematic formulation of questions and the application of mathematical assessments of probability and risk.

It should be said at once that the terms "lack of evidence", "not proven" or "there is insufficient evidence" are not equivalent to the terms "proven no effect" or "proven no benefit". The wording "not proven" may indicate a lack of knowledge of the problem and the feasibility of organizing larger studies or using other methods of collecting information and conducting statistical analysis. At the same time, we must not forget that the reverse wording "proven" may indicate statistical manipulation in the interests of manufacturing firms.

Evidence-based medicine is based on research methods used in epidemiology.

J.M. Last, formulating a modern definition of epidemiology, focuses on individual words in this definition. So, by "study" one should understand the conduct of observational (observational) and experimental studies, testing hypotheses and analyzing the results.
"The spread of diseases and factors ..." involves the study of the frequency of cases of illness, death, risk factors, the patient's compliance with doctor's recommendations, the organization of medical care and its effectiveness.
"Target group" - a group with the exact number of people and certain age, sex, social and other characteristics.

Currently, the modern concept of epidemiology is denoted by the term "clinical epidemiology". This term comes from the names of two "parent" disciplines: clinical medicine and epidemiology.
"Clinical" because it seeks to answer clinical questions and recommend clinical decisions based on the most reliable evidence.
"Epidemiology" because many of its methods are developed by epidemiologists, and care for a particular patient is considered here in the context of the large population to which the patient belongs.

Clinical Epidemiology- a science that allows prediction for each individual patient based on the study of the clinical course of the disease in similar cases using rigorous scientific methods of studying groups of patients to ensure accuracy of forecasts.

Purpose of clinical epidemiology- development and application of such methods of clinical observation, which make it possible to draw fair conclusions with a guaranteed assessment of the influence of systematic and random errors. This is the most important approach to obtaining the information doctors need to make the right decisions.

The fundamental method in epidemiology is comparison. It is carried out by mathematical calculations of such quantities as the odds ratio, the risk ratio of the development of the events under study.

However, before making a comparison, it is necessary to understand what we will compare with what (oranges with oranges, not oranges with steamboats), i.e. formulate a task (problem) that precedes the start of any research. Most often, the problem is formulated in the form of a question to which it is necessary to find an answer.

For example, hypothetically, we (that is, a practicing doctor) are presented with a drug that, according to the chemists who synthesized it, should treat the heel. The pharmacological company that put the production of the drug on stream also assures in the instructions that the claimed effect really takes place.

What can the practitioner do when deciding whether to use a drug?

The answer "take the word of chemists/pharmacologists" is excluded as trivial and fraught with consequences. Our task- check the claimed effect of the drug on the heel with the means available to the practitioner (confirm or refute, etc.). Of course, we will not test the drug on laboratory mice, volunteers, etc. It is assumed that before the "launch in the series" someone has already done this more or less conscientiously.

According to the task, we will begin the formation of an array of data that serves to solve it:

1. Let's search for information first.
2. Next, we exclude irrelevant articles from the resulting data array (irrelevant - inappropriate to our interests).
3. We will evaluate the methodological quality of the found studies (how correct is the method of collecting information in the study, are the statistical analysis methods used adequate, etc.) and rank the information in the resulting array according to the degree of evidence reliability based on existing medical statistics conventions and reliability criteria proposed by evidence-based medicine experts .

   According to the Swedish Council for Health Evaluation Methodology, the reliability of evidence from different sources is not the same, and depends on the type of study conducted. The type of study performed according to the international agreement of the Vancouver Group of Biomedical Editors (http://www.icmje.org/) must be carefully described; the methods of statistical processing of the results of clinical trials should also be indicated, conflicts of interest declared, the author's contribution to the scientific result and the possibility of requesting primary information from the author on the results of the study.

   To ensure the validity of the results obtained in studies, an "evidence-based", i.e., adequate to the tasks, research methodology (study design and statistical analysis methods) (Table 1) should be chosen, which we will take into account when selecting information from the data array.

   Table 1. The choice of research methodology depending on the purpose of the study
   (for a description of the terms, see the Glossary of methodological terms)

   Research objectives	Study Design	Methods of statistical analysis
   Estimating the prevalence of the disease	Simultaneous study of the entire group (population) using strict criteria for disease recognition	Share estimation, calculation of relative indicators
   Incidence assessment	cohort study	Share estimation, calculation of time series, relative indicators
   Assessment of risk factors for the onset of the disease	cohort studies. Case-control studies	Correlation, regression analysis, survival analysis, risk assessment, odds ratio
   Assessment of the influence of environmental factors on people, the study of cause-and-effect relationships in the population	Ecological studies of the population	Correlation, regression, survival analysis, risk assessment (added risk, relative risk, added population risk, added share of population risk), odds ratio
   Attracting attention to the unusual course of the disease, the result of treatment	Description of the case, series of cases	Not
   Description of the results of current clinical practice	Observational ("before and after")	Mean, standard deviation, paired Student's t-test (quantitative data). McNimar test (qualitative data)
   Testing a new treatment method	Phase I clinical trial ("before and after")	Mean, standard deviation, paired Student's t-test. McNimar criterion
   Comparison of two treatments in current clinical practice	controlled prospective. Randomized (open, blind, double blind). Controlled retrospective. Controlled prospective + retrospective (mixed design)	Student's criterion (quantitative data). Criterion χ 2 or z (qualitative features). Kaplan-Myers criterion (survival)
   Comparison of new and traditional method treatment	Clinical trials II-IV phases (controlled prospective or randomized)	Student's criterion. Criterion χ 2 . Kaplan-Myers criterion

   Each type of research is characterized by certain rules for collecting and analyzing information. If these rules are observed, any kind of research can be called qualitative, regardless of whether they confirm or refute the hypothesis put forward. More detailed statistical analysis methods used to obtain evidence are presented in the books of Petri A., Sabin K. "Visual statistics in medicine" (M., 2003), Glantz S. "Medical and biological statistics" (M., 1999).

   The degree of "proof" of information ranked as follows (in descending order):

   1. Randomized controlled clinical trial;
   2. Non-randomized clinical trial with simultaneous control;
   3. Non-randomized clinical trial with historical control;
   4. cohort study;
   5. "Case-control";
   6. Cross clinical trial;
   7. Observation results.

   The results of studies performed using simplified methods or methods that do not correspond to the objectives of the study, with incorrectly selected evaluation criteria, can lead to false conclusions.

   The use of complex evaluation methods reduces the likelihood of an erroneous result, but leads to an increase in the so-called administrative costs (data collection, database creation, statistical analysis methods).

   So, for example, in the study of E.N. Fufaeva (2003) revealed that among patients who had a disability group before surgery, the preservation of disability was registered in all 100%. Among patients who did not have a disability group before cardiac surgery, in 44% of cases after the operation, a disability group was determined. Based on this result, false conclusions can be drawn that cardiac surgery worsens the quality of life of patients. However, during the survey, it turned out that 70.5% of patients and 79.4% of doctors who observed these patients were satisfied with the results of treatment. Registration of a disability group is due to social reasons (benefits for receiving medicines, payment for housing, etc.).

   Significance social protection in matters of ability to work confirm the results of a study conducted in the United States and did not reveal a clear relationship between the clinical condition (somatic disease) of the patient and the ability to work.

   In order to compare employment rates after PTBA and CABG, 409 patients were examined (Hlatky M.A., 1998), 192 of them underwent PTBA and 217 underwent CABG. Patients who underwent PTBA were found to return to work six weeks faster than patients who underwent CABG. However, in the long term, the influence of such a factor as the type of operation turned out to be insignificant. Over the next four years, 157 patients (82%) in the TBA group and 177 patients (82%) in the CABG group returned to work. The strongest influence on the long-term employment rate was exerted by such factors as the age of the patient at the time of the start of the study and the degree of health insurance coverage of medical care.

   In this way, medical factors had a smaller impact on employment rates in the long run than demographic and social. The results obtained by Russian and American researchers indicate that some of the traditional and seemingly simple methods for assessing treatment outcomes are unacceptable for choosing priorities and making decisions.

4. After that, we will make a systematic review - meta-analysis, we will evaluate the level of reliability of the results obtained in the course of the research and compare: are there any advantages of the studied methods of diagnostics, treatment, methods of payment for services, targeted programs over those compared or previously used.

   If we include information with a low degree of certainty, then this point in our study must be discussed separately.

   The Center for Evidence-Based Medicine at Oxford, offers the following criteria for the reliability of medical information:

   • High Confidence- information is based on the results of several independent clinical trials with agreement between the results summarized in systematic reviews.
   • Moderate certainty- information is based on the results of at least several independent, similar clinical trials.
   • Limited certainty– information based on the results of one clinical trial.
   • There is no rigorous scientific evidence(clinical trials not conducted) - a certain statement is based on the opinion of experts.
5. And in conclusion, after evaluating the possibilities of using the results of the study in real practice, we will publish the result:

   This is a joke, of course, but there is some truth in every joke.

   Usually, studies are published that have brought positive results, for example, showing off a new treatment. If the working hypothesis (task, problem) is not confirmed or does not find a positive solution, then the researcher, as a rule, does not publish the research data. This can be dangerous. So, in the 80s of the twentieth century, a group of authors investigated an antiarrhythmic drug. In the group of patients who received it, a high mortality was found. The authors regarded this as an accident, and since the development of this antiarrhythmic drug was discontinued, they did not publish the materials. Later, a similar antiarrhythmic drug, flecainide, caused many deaths 1-2 .
   ________________________

   1. N Engl J Med. 1989 Aug 10;321(6):406-12, Preliminary report: effect of encainide and flecainide on mortality in a randomized trial of arrhythmia suppression after myocardial infarction. The Cardiac Arrhythmia Suppression Trial (CAST) Investigators.

The above algorithm for finding and evaluating evidence was proposed by D.L. Sackett et al (1997). It can be used in any study, even when evaluating the influence of the phases of the moon on the growth of telegraph poles.