Abstract 4180: Role of Gender, Age and MI Type on In Hospital Mortality Following Acute Myocardial Infarction: Results from 361,429 patients in NRMI 3–5
Bibliographic record
Abstract
Background: Previous reports have shown that younger women, but not older women, have higher mortality after MI compared to men, but studies have included mostly NSTEMI patients. We sought to determine gender differences in hospital mortality for both younger and older women according to MI type, e.g. STEMI and NSTEMI. Methods: We studied 126,172 STEMI and 235,257 NSTEMI patients enrolled in the NRMI registry between July 2000 and February 2006. Patients with preexisting LBBB and missing or normal troponin were excluded. Unadjusted and adjusted female to male relative risks (RR) for hospital death were calculated for 10-year age groups. Log-binomial models adjusted for demographic and clinical factors. Results : In both STEMI and NSTEMI, younger women, but not older women, had a higher unadjusted RR for hospital mortality than men. The RR decreased with increasing age (p<.01 for the age-gender interaction). Among NSTEMI, after adjusting for race, medical history, primary payor, and presentation characteristics there was no longer a significant gender difference in mortality in younger women, while older women (70+) had better survival compared to men (Figure ). Among STEMI, younger women continued to have a higher, albeit attenuated, risk for in-hospital mortality up to age 70, while there was no mortality difference between older men and women. Conclusions: Only younger women with STEMI, but not NSTEMI, have higher adjusted mortality compared with men. Older women with STEMI and NSTEMI have improved outcomes compared with men. Possible sex differences in pathophys-iology of younger women with STEMI and gender differences in treatment of this patient population should be explored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".