The need to reform our assessment of evidence from clinical trials: A commentary
Bibliographic record
Abstract
The ideology of evidence-based medicine (EBM) has dramatically altered the way we think, conceptualize, philosophize and practice medicine. One of its major pillars is the appraisal and classification of evidence. Although important and beneficial, this process currently lacks detail and is in need of reform. In particular, it largely focuses on three key dimensions (design, [type I] alpha error and beta [type II] error) to grade the quality of evidence and often omits other crucial aspects of evidence such as biological plausibility, reproducibility, generalizability, temporality, consistency and coherence. It also over-values the randomized trial and meta-analytical techniques, discounts the biasing effect of single centre execution and gives insufficient weight to large and detailed observational studies. Unless these aspects are progressively included into systems for grading, evaluating and classifying evidence and duly empirically assessed (according to the EBM paradigm), the EBM process and movement will remain open to criticism of being more evidence-biased than evidence-based."All scientific work is incomplete--whether it be observational or experimental. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time". (Sir Bradford Austin Hill 1).
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 | 0.199 | 0.634 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.018 | 0.007 |
| Research integrity | 0.068 | 0.096 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".