Reply to: Concerns about the Burden of Proof studies
Bibliographic record
Abstract
We, together with the Global Burden of Disease Study and Burden of Proof (BoP) collaborators, have published a BoP Capstone Methods paper and suite of associated meta-analyses that introduce and apply a new framework for synthesizing evidence to evaluate relationships between selected risk factors and health outcomes 1 , 2 , 3 , 4 , 5 . The BoP approach was designed to address key problems in current analytical frameworks. To provide helpful information to users making decisions around risk exposure, the approach systematically estimates a flexible mean risk–outcome function, avoiding making strong assumptions such as log-linearity of the relationship. Complementing the mean risk–outcome relationship, BoP analysis provides a BoP risk function (BoPRF) that represents the lowest estimate of excess harmful risk associated with a risk factor, incorporating unexplained between-study heterogeneity after accounting for known variation in study design characteristics. From the BoPRF, we calculate summary risk–outcome scores and star-rating measures that present conservative estimates of the risk–outcome relationship and enable comparisons across different risk–outcome pairs.
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 | 0.029 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.081 | 0.071 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".