Use of Generalized Linear Mixed Models to Examine the Association between Air Pollution and Health Outcomes
Bibliographic record
Abstract
BACKGROUND: Time-series and case-crossover are two techniques that are widely used for assessing the short-term impact of ambient air pollution exposure on health. MATERIALS AND METHODS: The generalized linear mixed model (GLMM) methodology is proposed here to study the association between ambient air pollution and health outcomes. Poisson random-effects models are applied to analyze the clustered counts, where the groups of days, determined by the triplet , form the clusters. The proposed technique uses a nested structure for the clusters and allows random-effects for hierarchical factors. A random intercept in the models adjusts for different levels of counts among the clusters. A fixed slope represents a common response to the exposure. RESULTS AND CONCLUSIONS: The obtained results are consistent with those generated by a classical approach (for example the case-crossover technique). The GLMM technique is a valid alternative methodology for studying air health effects.
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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 | 0.040 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".