Innovating Medical Knowledge: Understanding Evidence-Based Medicine as a Socio-Medical Phenomenon
Bibliographic record
Abstract
Evidence Based Medicine -Closer to Patients or Scientists?12 "Evidence based medicine," one chemist said to me, "What other kind of medicine could there possibly be?" and a consultant physician said gruffly: "We have always practiced evidence based medicine" (Hope, 1995). 1 The EBM pioneers equivocated on the movement's innovation and conservatism.It was described as both a "new paradigm" (EBMWG, 1992) and a historically-supported approach "whose philosophical origins extend back to mid-19th century Paris and earlier" (Sackett et al., 1996b).Yet it will be demonstrated in this chapter that although EBM is not best understood as a new "paradigm" or a radical departure from biomedicine, it offers methodological innovation that has shifted how we pursue, collect, and evaluate medical knowledge.Beginning with a historical account of the origins of EBM, a focus on three key methodological innovations employed by EBM will be used to advance the argument that EBM's original contribution to medicine, or what separates EBM from other approaches, is the priority it gives to certain forms of evidence, specifically evidence from randomized controlled trials.EBM offers a shift in the sort of evidence that is most highly valued for diagnosis, therapy, and prognosis questions, as heavy emphasis is placed on experimental controls and quantified measures, thus diminishing the previous status of clinical experience and observational studies significantly.This commitment represents not only methodological change, but also a novel regard of the reliability of various forms of medical knowledge.EBM offers a new answer to medicine's fundamental normative question: how ought we to practice medicine? How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following: Maya J. Goldenberg (2012).Innovating Medical Knowledge: Understanding Evidence-Based Medicine as a Socio-Medical Phenomenon, Evidence Based Medicine -Closer to Patients or Scientists?, Prof. Nikolaos Sitaras (Ed.),
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.051 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".