. Geographical variation and temporal trend of myocardial infarction hospital admissions in Calgary
Bibliographic record
Abstract
Coronary heart disease is a leading public health concern. The etiology of myocardial infarction may include both individual and contextual factors which are not fully accounted for in existing research. Spatial and temporal analysis of disease data provids a strategy for identifying influencing contextual factors in areas with high MI disease burden and may generate new hypotheses on determinants of the disease. Temporal analysis is applied to investigate the temporal trends in monthly age and gender stratified myocardial infarction hospitalizations from 2004 to 2013 in Calgary. Ripley’s K function is performed for spatial pattern analysis, and complemented by hot spot analysis to identify MI clusters. The trend analysis exhibits a statistically significant declining trend for all the goups. Most groups show a peak of MI occurrence in fall and winter. The group over 75 features the highest incidence of MI, and males account for a larger proporation of MI. Spatial analysis suggests MI incidence is clustered in communities with a larger proportion of older people, lower socioeconomic status, and closer to the airport and industrial areas.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 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".