Understanding Hierarchies of Evidence and Grades of Recommendation
Bibliographic record
Abstract
Hierarchies of evidence have been established to assist clinicians in judging the overall quality of studies and for developing grades of recommendation. These hierarchies take into account both study design and study methodology, among other characteristics, to rank studies into several levels. Studies of a sound design and methodological rigor are less subject to bias and rank highly in the hierarchy, whereas those studies vulnerable to biased results rank progressively lower in the hierarchy. It is on the basis of these hierarchies that grades of recommendation and clinical practice guidelines can be established to facilitate the practice of evidence-based medicine.
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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 | 0.346 | 0.797 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.067 | 0.029 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".