Association between quantity and duration of snowfall and risk of myocardial infarction
Bibliographic record
Abstract
BACKGROUND: Although aggregate data suggest a link between snowfall and myocardial infarction (MI), individual risk has yet to be assessed. We evaluated the association between quantity and duration of snowfall and the risk of MI using nonaggregated administrative health data. METHODS: We used a case-crossover study design to investigate the association between snowfall and hospital admission or death due to MI in the province of Quebec, Canada, between November and April during 1981-2014. The main exposure measures were quantity (in centimetres) and duration (in hours) of snowfall by calendar day. We computed odds ratios (ORs) and 95% confidence intervals (CIs) for the association between daily snowfall and MI, adjusted for minimum daily temperatures. RESULTS: In all, 128 073 individual hospital admissions and 68 155 deaths due to MI were included in the analyses. The likelihood of MI was increased the day after a snowfall among men but not among women. Compared with 0 cm, 20 cm of snowfall was associated with an OR of 1.16 for hospital admission (95% CI 1.11-1.21) and 1.34 for death (95% CI 1.26-1.42) due to MI the following day among men. Corresponding ORs among women were 1.01 (95% CI 0.95-1.07) and 1.04 (95% CI 0.96-1.13). Similar but smaller associations were observed for snowfall duration (0 h v. 24 h) and MI. INTERPRETATION: Both the quantity and duration of snowfall were associated with subsequent risk of hospital admission or death due to MI, driven primarily by an effect in men. These data have implications for public health initiatives in regions with snowstorms.
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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 | 0.003 |
| 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) | 0.001 | 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".