Myocardial infarction in Québec rural and urban populations between 1995 and 1997.
Bibliographic record
Abstract
INTRODUCTION: There is abundant evidence of health inequities between urban and rural populations. The purpose of this paper is to describe the socioeconomic characteristics of Québec urban and rural populations and the relation between rurality and incidence of myocardial infarction (MI), care management and outcomes. METHODS: Socioeconomic data by census subdivisions were available from the 1996 Canadian census, representing 7,137,245 individuals. Data on patients with MI were taken from the provincial administrative health database (MED-ECHO), which is managed by the Ministry of Health and contains clinical and demographic information collected when patients are released from acute care hospitals in Québec. RESULTS: We included a total of 37,678 cases compiled over the 3 years of follow-up in the analyses. Residents of rural areas with low urban influence have higher MI incidence rates than all of the other populations in the study. In comparison with urban populations, their observed rural counterparts are at a disadvantage with regard to education, employment and income. Although angioplasty and coronary artery bypass graft surgery rates were higher in more urban areas, the survival rate was lower than in rural areas. CONCLUSION: This study revealed geographic heterogeneity of MI incidence, revascularization rates and survival rates among urban and rural populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".