Weather and risk of ST-elevation myocardial infarction revisited: Impact on young women
Bibliographic record
Abstract
BACKGROUND: During the last decade, the incidence and mortality rates of ST-elevation myocardial infarction (STEMI) has been steadily increasing in young women but not in men. Environmental variables that contribute to cardiovascular events in women remain ill-defined. METHODS AND RESULTS: A total of 2199 consecutive patients presenting with acute ST-elevation myocardial infarction (STEMI, 25.8% women, mean age 62.6±12.4 years) were admitted at the Montreal Heart Institute between June 2010 and December 2014. Snow fall exceeding 2cm/day was identified as a positive predictor for STEMI admission rates in the overall population (RR 1.28, 95% CI 1.07-1.48, p = 0.005), with a significant effect being seen in men (RR 1.30, 95% CI 1.06-1.53, p = 0.01) but not in women (p = NS). An age-specific analysis revealed a significant increase in hospital admission rates for STEMI in younger women ≤55 years, (n = 104) during days with higher outside temperature (p = 0.004 vs men ≤55 years) and longer daylight hours (p = 0.0009 vs men ≤55 years). Accordingly, summer season, increased outside temperature and sunshine hours were identified as strong positive predictors for STEMI occurrence in women ≤55 years (RR 1.66, 95% CI 1.1-2.5, p = 0.012, RR 1.70, 95% CI 1.2-2.5, p = 0.007, and RR 1.67, 95% CI 1.2-2.5, p = 0.011, respectively), while an opposite trend was observed in men ≤55 years (RR for outside temperature 0.8, 95% CI 0.73-0.95, p = 0.01). CONCLUSION: The impact of environmental variables on STEMI is age- and sex-dependent. Higher temperature may play an important role in triggering such acute events in young women.
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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 | 0.001 |
| 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".