Concerns about the Burden of Proof studies
Bibliographic record
Abstract
We read with interest the Burden of Proof (BoP) studies 1 , 2 , 3 , 4 , 5 in which the authors conducted meta-analyses of epidemiological studies to provide an overall conservative quantitative assessment for several important public health questions. For ease of interpretation, they transformed the overall assessment into a star rating (1–5 stars). Examples include five stars for smoking and lung cancer, two stars for low vegetable intake and ischemic heart disease (IHD) and two stars for unprocessed red meat and type 2 diabetes (T2D), colorectal cancer and IHD 6 . They used this same method to assign just three stars to smoking in relation to IHD and one or two stars to other well-established relationships 1 , 2 , 3 , 4 . However, we believe there are serious methodological issues with their meta-analyses 5 ; the star rating of evidence strength is overly simplistic and could cast doubt on existing recommendations and policies intended to prevent chronic disease and treat illnesses.
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 | 0.374 | 0.771 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.014 | 0.010 |
| Research integrity | 0.079 | 0.092 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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".