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Record W2072653194 · doi:10.1353/pbm.0.0089

Evidence-Based Medicine Again

2009· article· en· W2072653194 on OpenAlexaboutno aff
Alan N. Schechter, Robert L. Perlmanan

Bibliographic record

VenuePerspectives in biology and medicine · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
Fundersnot available
KeywordsCriticismEvidence-based medicineTerminologyAlternative medicineValue (mathematics)MEDLINEMedicineEngineering ethicsEpistemologyPhilosophyPolitical scienceLawComputer sciencePathology

Abstract

fetched live from OpenAlex

Evidence-Based Medicine Again Alan N. Schechter and Robert L. Perlmanan This issue of Perspectives in Biology and Medicine presents a group of papers in a symposium entitled “The Nature of Evidence in Evidence-Based Medicine,” which was conceived and edited by Robyn Bluhm, Kirstin Borgerson, and Maya Goldenberg. Two of these editors, Bluhm and Borgerson, also edited a collection of articles on evidence-based medicine (EBM) that we published in 2005 (48:475–584). We appreciate the efforts of the editors and all of the contributors in presenting clearly and succinctly their analyses and critiques of EBM. We believe strongly that EBM has been of major value to medical practice, especially with regard to screening methodologies and therapeutics, and we welcome these critiques as constructive criticism, designed to strengthen, rather than disparage, the practice of EBM. Because much of the terminology and ardor of this field has developed since several seminal papers were published in the 1980s, EBM is commonly thought to be a new discipline; nonetheless, its antecedents can clearly be traced back at least to several controlled or numeric studies now regarded as epic, such as those of James Lind in the 18th century and John Snow in the 19th. It was not until the mid-20th century, however, that statistical methods, including randomized controlled trials (RCTs) and outcomes research, were sufficiently developed and applied to medical problems to have resulted in approaches to epidemiology and therapeutics that we would recognize as modern in their scientific rigor. About then the pioneering work of several physicians, such as Alvan Feinstein at Yale and Archie Cochrane at Cardiff, led to the creation of the discipline of clinical [End Page 161] epidemiology and began a more general program of systematically applying these new methods to medicine. It was, however, David Sackett and his colleagues —initially at McMaster University in Canada and then at Oxford—who turned this academic discipline into a “movement,” perhaps largely by giving it the catchy name of evidence-based medicine. Clearly there was an internationally perceived need for this development, and in the last 15 years EBM has taken off and rapidly expanded, with innumerable articles, monographs, textbooks, and now journals devoted to it. Numerous private, quasi-governmental, and governmental organizations—such as the Cochrane Collective, the U.S. Preventive Services Task Force, and various ad hoc “working groups”—have appeared in order to develop and implement these ideas. These groups, in various countries and some on an international scale, have developed tools such as “guidelines” for medical care, sometimes accompanied by strong organizational or even governmental mandates for their implementation. Not surprisingly, these developments have precipitated something of a backlash. These negative reactions have ranged from those who argue that informed physicians have always tried to use the best evidence in making clinical decisions to those who are perturbed by the implicit assumption of most EBM models that knowledge of mechanisms of disease is not sufficient for making decisions about medical practice, as compared to evidence from various types of clinical trials. The last assumption unfortunately has been interpreted as calling into question the implicit basis of much funding of biomedical research, the search for molecular and cellular mechanisms, especially when—as now—there is increased competition for research funds. Other negative reactions have focused on more limited aspects of the EBM paradigm, particularly on the notion of assigning relative values to different types of evidence and the idea that the RCT is indeed the gold standard for judging any intervention. Critics of these and other aspects of the EBM program range from those who approach these issues as philosophers to those who have experienced them as practicing physicians. While we are convinced that evidence-based guidelines and checklists have improved patient care, we agree that there are ways in which the implementation and practice of EBM need to be strengthened. The evidence base of EBM still suffers from a variety of biases, ranging from individual conflicts of interest to unwarranted corporate intrusion into the design and publication of the results of clinical trials; these biases need to be eliminated to the extent possible, and recognized if they cannot be eliminated. Although RCTs are an...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.068
metaresearch head score (Gemma)0.108
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0680.108
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.743
GPT teacher head0.598
Teacher spread0.145 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2009
Admission routes1
Has abstractyes

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