Bias correction in estimation of public health risk attributable to short‐term air pollution exposure
Why this work is in the frame
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Bibliographic record
Abstract
Numerous epidemiologic studies have reported associations between short‐term air pollution exposure and mortality. Such short‐term risk models include smooth functions of time to control for unmeasured confounding variables. We demonstrate bias in these short‐term Generalized Additive Model estimates because of lack of accounting for long timescale variations and propose a family of improved time smoothers to reduce and control the bias. The strengths of the proposed smoother are twofold: a clear separating of short‐term and long‐term effects and an obvious choice of smoothing parameters from pre‐determined timescales of interest. We demonstrate improvements through simulations and analysis of examples of air pollution and mortality data from Chicago, Il. from the National Morbidity, Mortality and Air Pollution Study database, showing reduced bias in the risk estimates. © 2015 The Authors. Environmetrics Published by John Wiley & Sons Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it