Comparison of transient associations of air pollution and AMI hospitalisation in two cities of Alberta, Canada, using a case-crossover design
Bibliographic record
Abstract
OBJECTIVE: To investigate reproducibility of outcomes for short-term associations between ambient air pollutants and acute myocardial infarction (AMI) hospitalisation in 2 urban populations. DESIGN: Using a time-stratified design, we conducted independent case-crossover studies of AMI hospitalisation events over the period 1999-2010 in the geographically close and demographically similar cities of Calgary and Edmonton, Alberta, Canada. Patients with his/her first AMI hospitalisation event were linked with air pollution data from the National Ambient Pollution Surveillance database and meteorological data from the National Climatic Data Center database. Patients were further divided into subgroups to examine adjusted pollution effects. Effects of pollution levels with 0-3-day lag were modelled using conditional logistic regression and adjusted for daily average ambient temperature, dew point temperature and wind speed. SETTING: Population-based studies in Calgary/Edmonton. PARTICIPANTS: 12,066/10,562 first-time AMI hospitalisations in Calgary/Edmonton. MAIN OUTCOME MEASURES: Association (adjusted OR) between daily ambient air pollution levels and hospitalisation for AMI. RESULTS: Among 600 potential air pollution effect variables investigated for the Calgary (Edmonton) population, only 1.17% (0.67%) was statistically significant by using the traditional 5% criterion. None of the effect variables were reproduced in the 2 cities, despite their geographic closeness (within 300 km of each other), and demographic and air pollution similarities. CONCLUSIONS: Comparison of independent investigations of the effect of air pollution on risk of AMI hospitalisation in Calgary and Edmonton, Alberta, indicated that none of the air pollutants investigated-CO, NO, NO2, O3 and particulate matter (PM2.5)-showed consistent positive associations with increased risk of AMI hospitalisation.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".