A quantitative burden of proof risk function to evaluate environmental risk factors in comparative risk assessments
Bibliographic record
Abstract
Background and Aims Assessing strength and quantifying risk-outcome relationships is critical for public health prioritization, policy formulation, clinical guidance and to inform personal choices related to modifiable risk factors. While meta-analyses or meta-regressions are often used as an inputs, their use in decision making is often subjective. We introduce the quantitative burden of proof risk function in the context of the Global Burden of Disease comparative risk assessment framework. Methods The burden of proof risk function combines meta-regression mean relationship between exposure and risk with unexplained between-study heterogeneity, adjusted for number of studies. We developed and applied a Bayesian meta regression framework to robustly estimate the mean and burden of proof risk function, allowing for non-linear relationships, and applied this to 197 behavioral, metabolic and environmental risk factor – outcome relationships. Relationships were summarized by a ‘star-rating’ where 1-star risks had a probability of no association after accounting for between-study heterogeneity, and 2, 3, 4, and 5-star risks indicated a 0-15%, >15-50%, >50-85% and >85% increase in risk, respectively, over the 15%-85% percentiles of exposures of included studies. Results Environmental risk factors included in the Global Burden of Disease ranged from 4-star (e.g. sanitation-diarrheal disease) to 1-star (e.g. NO2 – asthma). Across all 197 included risk-outcome pairs, 4%, 7%, 25%, 42% and 23% were 5, 4, 3, 2, and 1-star, respectively. 81% of the 32 included environmental risk factor-pairs, were 2 or 3-star. Conclusions The burden of proof risk function is a cautious interpretation of evidence, incorporating both magnitude and uncertainty in relative risks. Higher risk scores indicate a larger effect and/or a lower probability of results driven by residual confounding or other bias. Risk-outcome pairs with low star ratings indicating substantial between-study heterogeneity may suggest a need for additional studies, especially where exposure and outcome prevalence are high.
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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 | 0.281 | 0.617 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".