Air pollution and emergency department visits for chest pain and weakness in Edmonton, Canada
Bibliographic record
Abstract
OBJECTIVES: Chest pain or weakness can be first signal of health problems. Many studies demonstrate that these conditions can be related to air pollution. This work uses time-series data to investigate the association. MATERIAL AND METHODS: This is a study of 68,714 emergency department (ED) visits for chest pain (ICD-9: 786) and of 66,092 ED visits for weakness (ICD-9: 780). The hierarchical method was applied to analyse the associations between daily counts of ED visits for chest pain and weakness (separately) and the levels of the air pollutants and meteorological variables. The counts of visits for all patients, males and females were analysed separately by whole period (I-XII), warm (IV-IX) and cold (X-III). RESULTS: The results are presented in the form of the excess risks associated with an increase in the interquartile range (IQR) for the pollutant. Chest pain: 2.4% (95% CI: 1.0-3.9) for CO, females, I-XII; 3.8% (95% CI: 0.0-7.8) for NO(2), males, IV-IX; 4.5% (95% CI: 0.9-8.3) for O(3) (1-day lagged), males, IV-IX; 2.8% (95% CI: 0.5-5.2), for PM(10), males, X-III; 2.0% (95% CI: 0.0-4.0), for SO(2), females, X-III; 2.1% (95% CI: 0.2-4.0) for PM(2.5), all, X-III. Weakness: 2.1% (95% CI: 0.4-3.7) for CO (2-day lagged), males, X-III; 3.4% (95% CI: 1.0-5.9) for NO(2) (2-day lagged), males, X-III; 2.4% (95% CI: 0.9-3.9) for SO(2), females, I-XII; 4.6% (95% CI: 1.0-8.2) for O(3) (1-day lagged), females, IV-IX. CONCLUSIONS: Obtained findings provide support for the hypothesis that ED visits for chest pain and weakness are associated with exposure to ambient air pollution.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".