Air Pollution and Daily Emergency Department Visits for Headache in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Many studies have indicated that weather can trigger headache. Here we propose a new methodological approach to assess the relationship between weather, ambient air pollution, and emergency department (ED) visits for this condition. OBJECTIVE: To examine the associations between ED visits for headache and selected meteorological and air pollution factors. DESIGN AND METHODS: A hierarchical clusters design was used to study 10,497 ED visits for headache (ICD-9: 784) that occurred at a Montreal hospital between 1997 and 2002. The generalized linear mixed models technique was applied to create Poisson models for the clustered counts of visits for headache. RESULTS: Statistically significant positive associations were observed between the number of ED visits for headache and the atmospheric pressure for all and for female visits for 1-day and 2-day lagged exposures. The percentage increase in daily ED female visits was 4.1% (95% CI: 2.0, 6.2), 3.4% (95% CI: 1.4, 5.6), and 2.2% (95% CI: 1.4, 5.6) for current day, 1-day and 2-day lagged exposure to SO(2), respectively, for an increase of an interquartile range (IQR) of 2.4 ppb. The percentage increase was also statistically significant for current day and 1-day lagged exposure to NO(2) and CO for all and for female visits. CONCLUSIONS: Presented findings provide support for the hypothesis that ED visits for headache are correlated to weather conditions and ambient air pollution - to atmospheric pressure and exposure to SO(2), NO(2), CO, and PM(2.5). An increase in levels of these factors is associated with an increase in the number of ED visits for headache.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".