Georg Ivanovas From Autism to Humanism - systems theory in medicine

2.5 The limits of evidence based medicine

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i) conclusion

Taking all aspects together it can be shown that “most published research findings are false for most research designs and for most fields” and the results may often be simply an accurate measure of the prevailing bias (Ioannidis 2005). „Randomized trials ‘are very good for showing that a drug does what the pharmaceutical company says it does,’ said David Atkins, a preventive-medicine specialist at the Agency for Healthcare Research and Quality, ‘but not very good for telling you how big the benefit really is and what are the harms in typical people. Because they don’t enroll typical people’” (Taubes 2007).

Rarely a therapy has been tested so intensively as hormone replacement therapy. But even here, or especially here observational studies contradict randomised trials and a minute investigation shows that the influencing factors are so numerous that no definite answer is possible (Taubes 2007). At most, very prominent relations like smoking and lung cancer can be convincingly established thorough trials.

Therefore, the translation of statistical evidence into medical practice has to be made with a lot of care and a fair bit of doubt, as too often the truth of today turns out to be a mistake tomorrow. „Evidence based practice is not practice directed by research evidence. Evidence based practice is the judicious use of research evidence, based on a clinician's expertise and experience, in light of the patient's preferences. Research evidence does not supersede the challenging role of the doctor in clinical decision making, but it can support it. Just as diagnostic tests provide additional, helpful information but don't dictate patient management, research evidence provides further, hopefully useful information, but can't and shouldn't dictate practice. As both a patient and an evidence based practice researcher, I fervently hope that doctors still observe and think” (Turner 2004).

Bernard stated, nearly 150 years ago: “It is said that coincidence may play so large a part in causes of statistical errors, that we should base conclusions only on large numbers. But physicians have nothing to do with what is called the law of large numbers, a law which, according to a great mathematician’s expression, is always true in general and false in particular” (Bernard: 138).


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