Geographical variations in post myocardial infarction mortality and their impact on risk selection.
Bibliographic record
Abstract
OBJECTIVES: The objective is to assess the impact of geographical variations in mortality on risk selection in patients after acute myocardial infarction. METHOD: Mortality analysis is used with an actuarial methodology applied to follow-up studies based on data from randomized clinical trial and observational cohort studies of acute myocardial infarction patients from different geographic areas. Observed mortality was calculated as geometric average annual rate (q) and compared to the expected geometric average annual mortality (q') mortality calculated from different life tables. This comparison was expressed as mortality ratios (MR). Values of q and MR were averaged within each country grouping. Variance, standard deviation, and variation coefficient (CV) were calculated. RESULTS: Geometric average annual mortality rates varied by country. The lowest rate (2.7%) was observed in Japan, and the highest rates (7.5%) were seen in studies from the United Kingdom and Northern Europe (Denmark, Sweden, Finland). The average annual mortality rate was 4.9%. Mortality ratios averaged within countries vary from 182% to 212%, with an overall average value of 198%. Coefficient of variation (CV) was 36% for geometric average annual mortality rates and 6% for mortality ratios. CONCLUSION: Although annual mortality rates from all causes vary greatly between countries, mortality ratios do not vary and remain relatively constant. This highlights the interest of risk assessment using mortality analysis methodology, which makes the geographic variation in post-myocardial infarction mortality disappear.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".