Understanding of research results, evidence summaries and their applicability—not critical appraisal—are core skills of medical curriculum
Bibliographic record
Abstract
To practice high quality healthcare, clinicians must be able to diagnose correctly, provide preventative and treatment interventions based on the best available evidence, and ensure decisions are consistent with patients’ values and preferences. The educational approaches to teaching evidence-based medicine (EBM) to ensure the clinical decisions reflect both the best evidence and patients’ values are, however, open to question. EBM experts devoted to optimising EBM education often suggest that to practice high-value, evidence-based care requires ensuring that clinicians are able to critically appraise original research studies, as well as systematic reviews. Critical appraisal includes addressing risk of bias, and that involves a careful reading of methods and results. If indeed optimal practice requires such critical appraisal, it naturally follows that in introducing EBM one should educate clinicians so that they can competently make risk of bias assessments of randomised trials and observational studies, and similarly assess the rigour of systematic reviews. Much—perhaps almost all—of the EBM educational community has adopted this position and, therefore, EBM lectures and workshops often have their primary focus on critical appraisal. These sessions usually involve detailed assessment of risk of bias by careful, critical reading of methods and results of research studies. The Centre for Evidence-Based Medicine website,1 presents critical appraisal as the systematic evaluation of clinical research papers and aims to answer the following questions: (1) does this study address a clearly focused question? (2) did the study use valid methods to address this question? (3) are the valid results of this study important? and (4) are these valid, important results applicable to my patient or population? If the answer to any of these questions is ‘no’, it is also stated on the website that ‘you can save yourself the trouble of reading the rest of it’. The second criterion represents the risk …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.313 | 0.587 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.026 | 0.027 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".