Do “Evidence-Based Recommendations” Need to Reveal the Evidence? Minimal Criteria Supporting an “Evidence Claim”
Bibliographic record
Abstract
“Evidence-based medicine” (EBM) stresses examining evidence from clinical research1 as the preferred method of clinical decision making, de-emphasizing intuition, unsystematic clinical experience, and pathophysiologic rationale. Likewise, evidence-based practice (EBP) holds that evidence should be the basis for particular interventions and management plans that are likely applicable to most patients. Logically, this practice would demand that experts develop evidence-based recommendations founded on valid, reliable, and transparent systematic reviews and/or metaanalyses2,3. In this issue of The Journal , Roubille, et al present recommendations for the management of comorbidities, focusing on 8 areas within rheumatoid arthritis (RA), psoriasis (PsO), and psoriatic arthritis (PsA), based on a review of 407 articles4. Their report summarizes the results of the Canadian Dermatology-Rheumatology (DR) Comorbidity Initiative’s systematic literature searches and consensus-based recommendations from a meeting held in Toronto in 2013, sponsored by the pharmaceutical company AbbVie4. The authors report that they did a thorough, systematic review, followed by data extraction and subsequent metaanalyses (including forest plots summarizing the adjusted relative risk estimates, etc.). However, the authors apparently do not want to reveal their explicit findings (yet!), stating that the details and results of the systematic literature review for each topic will be published separately4. We have no reason to question the integrity or the content of the recommendations that came out of this work, but we worry about a possible trend that could encourage guideline panels, etc., to publish their evidence-synthesis secondarily to their recommendation while claiming in a peer-reviewed journal (like The Journal of Rheumatology ) that their work represents evidence-based recommendations. Although realizing that we might be perceived as having intellectual conflicts of interest (i.e., being editors in the Cochrane Collaboration), we would like to encourage systematic and explicit methods of making judgments because they … Address correspondence to Prof. Christensen, Copenhagen University Hospital at Frederiksberg, Musculoskeletal Statistics Unit, The Parker Institute, Nordre Fasanvej 57, Copenhagen F, DK-2000, Denmark. E-mail: robin.christensen{at}regionh.dk
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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 | 0.312 | 0.771 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.017 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.021 | 0.032 |
| Open science | 0.016 | 0.009 |
| Research integrity | 0.040 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".