Temporal analysis of acute myocardial infarction in Ontario, Canada.
Bibliographic record
Abstract
BACKGROUND: Acute myocardial infarction (AMI) is a substantial cause of morbidity and mortality in Canada. Evidence suggests that the incidence and mortality of AMI increase in the winter. Determining the strength and nature of seasonality patterns in relation to age and sex may be helpful in health care planning. OBJECTIVES: To examine the seasonal patterns of AMI hospital admissions by age and sex, to assess the strength of the seasonal patterns and to examine the overall trends in admissions. METHODS: A retrospective population-based study was conducted to assess temporal patterns in 14 years of hospital admissions for AMI (from April 1, 1988, to March 31, 2002) in Ontario. Seasonality was assessed using the autoregression coefficient (R2Autoreg), and Fisher's Kappa and Bartlett's Kolmogorov-Smirnov tests. RESULTS: There were 271,321 people in the cohort, of whom 63% (n = 171,546) were male and 37% (n = 99,775) were female. There was an increase in AMI admissions since 1988 that reached a plateau in 1992, which was attributable mostly to the increased rate in the oldest age groups (70 years and older), where admission rates more than doubled. An association between seasonality and AMI admissions was found in most age and sex groups, with men consistently exhibiting a stronger seasonality pattern. The greatest difference in the cohort, 2.5 per 100,000 per month (134 admissions), occurred between December and September (13.64 per 100,000 in September versus 16.14 per 100,000 in December). CONCLUSIONS: AMI admissions show seasonality patterns, which are more pronounced in men. Although statistically significant, the seasonal differences are small in terms of absolute numbers, and are likely irrelevant in health care planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".