Revascularizing coronary artery disease in patients undergoing transcatheter aortic valve implantation
Bibliographic record
Abstract
Aims We investigate sex disparities in management and outcomes of myocardial infarction (MI) in contemporary practice in Scotland. Methods and results This was a longitudinal cohort study including all MI admissions aged 45-80 years across Scotland between 2010–2016 and 2:1 age, sex, and general practice-matched general population controls. Participants were followed up until the end of 2021. We analysed in-hospital outcomes (percutaneous coronary intervention, secondary prevention and mortality) using Poisson regressions, adjusting for age, comorbidities, and ST-elevation. We used Royston–Parmar models for long-term outcomes (all-cause and cardiovascular mortality, incident cardiovascular events), adjusting for age, comorbidities, and secondary prevention. Of a total 47 063 MI patients, 15 776 (33.5%) were women. Median (inter-quartile range) age was 66 (57, 73) years. Compared to men, women were older and more comorbid, but were less likely to undergo percutaneous coronary intervention [risk ratio (95% confidence interval) − 0.87 (0.86 – 0.89)] or receive secondary prevention at discharge [0.94 (0.93–0.95)]. No in-hospital mortality difference was observed between sexes [1.06 (0.99–1.13) after adjustment]. Over a median follow-up of 8.2 (6.7, 10.1) years, women had higher crude rates of adverse outcomes. After full adjustment, this translated into a lower risk for women compared to men of all-cause mortality [hazard ratio, 0.92 (0.89–0.95)], cardiovascular mortality [0.82 (0.78–0.87)], and cardiovascular events [0.92 (0.88–0.95)]. The female survival advantage seen in general population controls was attenuated in MI patients. Conclusion Women were undertreated compared to men after MI. Their survival and outcome benefits may be improved further. Poor outcomes in men despite better receipt of secondary prevention require further attention.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".