Evidence-Based Medicine 20 Years On: A View from the Inside
Bibliographic record
Abstract
In this this issue, Seshia & Young report a comprehensive review of the literature 1,2 addressing the evolution of the Evidence-based Medicine (EBM) paradigm.3 In addition to illustrating the profusion of concepts and terms associated with EBM, the authors have highlighted the current controversies in this area and demonstrated that, for many issues, there are no definitive answers.Although useful, there is a risk that a puzzled reader may be left with nothing but uncertainties.In this editorial we will focus on the current challenges that clinicians face when trying to apply EBM in practice, and how recent developments can help them meet these challenges.Clinicians must find the current best evidence to answer their questions.In parallel to the widespread uptake of EBM, the volume of research has been dramatically increasing, with now more than 2000 articles published in MEDLINE every day, including 75 randomized controlled trials.4 Clinicians therefore need resources that filter, appraise, and synthesize the evidence and facilitate access to this processed information at the point of care.Such resources include systematic reviews, synopses of high quality studies or reviews (often published in evidencebased journals, such as ACP Journal Club), online evidencebased textbooks (e.g.UpToDate, Dynamed), and clinical practice guidelines.5 As pointed out by Seshia and Young 2 , some evidence is more trustworthy than other, and evidence summaries must distinguish between the more and less trustworthy.Whoever makes this assessment must be familiar with many evolving methodological and statistical concepts often far from the expertise of individual clinicians (think for example of non-inferiority trials with
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 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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.086 | 0.045 |
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".