A Review of Time-Series Studies Used to Evaluate the Short-Term Effects of Air Pollution on Human Health
Why this work is in the frame
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Bibliographic record
Abstract
We review the methodology used in the analysis of time-series studies of ambient air pollution. Our focus is on mortality studies, in which daily counts of death are correlated with changes in air pollution. We first illustrate the methods by showing data from the 1950s, during which the effects of air pollution were much more pronounced, and then describe current methods that were developed to identify associations when the signal-to-noise ratio is much lower. We describe basic data sources, details of statistical methods, and current state of the art, especially as it refers to problems found recently with the fitting algorithm used in the generalized additive models. A summary of the findings from mortality studies is presented and the pre-eminent issues regarding methods, interpretation, and identification of susceptible populations are discussed. We conclude by describing possible biological mechanisms and suggesting other designs that will aid in the interpretation of data from studies of acute health effects.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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