Air Pollution and Emergency Department Visits for Headache and Migraine
Bibliographic record
Abstract
Background: Associations between ambient air pollution and emergency department (ED) visits for headache and migraine were examined in a multi-city study during the period of April 2004 to December 2011 in nine cities across Ontario, Canada. Objectives: Evaluate potential positive associations between air pollution and ED visits for headache. Materials and Methods: Data on ED visits for headache were retrieved from the national ambulatory care reporting system in Canada. Case-crossover design was used for this study for three diagnosis categories: migraine, headache-OS (other specified, OS) and headache-NOS (not otherwise specified, NOS). A time-stratified case-crossover technique was applied to investgate the associations of ED visits for headache with ambient air pollution. Odds ratios (ORs) and their corresponding 95% confidence intervals for ED visits associated with increased levels of air pollutants were calculated by applying conditional logistic regression. Results: Among females, statistically significant positive results were observed for one unit increase in inter-quartile range (IQR) of NO2 (IQR = 9 ppb) for lag 0 days: OR = 1.015 (1.000, 1.030) for migraine and for NOS: for NO2 for lags 0 to 2, where the highest result was for lag 0: OR = 1.015 (1.005, 1.026), for SO2 (IQR = 2.5 ppb) for lag 2: OR = 1.012 (1.002, 1.021) and for PM2.5 for lags 1 and 2, OR = 1.011 (1.002, 1.021) and OR = 1.010 (1.000, 1.020) respectively among females. No significant statistically significant results were observed among males. Conclusions: Our findings support a number of statistically significant positive associations between air pollutants and the number of ED visits for headache and migraine.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".