Meta‐Analysis Methods to Estimate the Shape and Uncertainty in the Association Between Long‐Term Exposure to Ambient Fine Particulate Matter and Cause‐Specific Mortality Over the Global Concentration Range
Bibliographic record
Abstract
Estimates of excess mortality associated with exposure to ambient concentrations of fine particulate matter have been obtained from either a single cohort study or pooling information from a small number of studies. However, standard frequentist methods of pooling are known to underestimate statistical uncertainty in the true risk distribution when the number of studies pooled is small. Alternatively, Bayesian pooling methods using noninformative priors yield unrealistically large amounts of uncertainty in this case. We present a new hybrid frequentist-bayesian framework for meta-analysis that incorporates features of both frequentist and Bayesian approaches, yielding estimated uncertainty distributions that are more useful for burden estimation. We also present an example of mortality risk due to long-term exposure to ambient fine particulate matter obtained from a small number of cohort studies conducted in the United States and Europe. We compare our new risk uncertainty distribution to that obtained by the integrated exposure-response (IER) model used in the Global Burden of Disease 2010 project for which risk was modeled over the entire global concentration range. We suggest a method to incorporate our new risk uncertainty distribution based on the relatively low concentrations observed in the United States and western Europe into the IER model, thus extending risk estimation to the global concentration range.
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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 | 0.066 | 0.165 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".