Evaluating the quality of medical evidence in real‐world contexts
Bibliographic record
Abstract
How should the quality of medical evidence be evaluated? Proponents of evidence-based medicine advocate the use evidence hierarchies to rank the quality of evidence on the basis that certain methods produce more reliable evidence. Some criticisms of this approach focus on whether certain methods deserve their place in the hierarchy, while others claim that evidence hierarchies should be abandoned in favour of other evidence assessment techniques. We claim that this debate pays insufficient attention to the real-world contexts in which medical decisions are made. To address this limitation, we explore the value of using evidence hierarchies and other evidence assessment techniques in differing contexts of medical decision making and argue that the way in which the quality of medical evidence should be evaluated depends on context. Focusing the discussion of the evaluation of medical evidence on real-world contexts has implications for the viability of the principle of total evidence.
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 | 0.369 | 0.713 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.033 | 0.030 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".