The Intersection of Socioeconomic Differences and Sex in the Management and Outcomes of Acute Myocardial Infarction: A Nationwide Cohort Study
Bibliographic record
Abstract
Patients with lower socioeconomic status (SES) have poorer outcomes following acute myocardial infarction (AMI) than patients with higher SES; however, how sex modifies socioeconomic differences is unclear. Using the United Kingdom (UK) Myocardial Ischaemia National Audit Project (MINAP) registry, alongside Office of National Statistics (ONS) mortality data, we analyzed 736,420 AMI patients between 2005 and 2018, stratified by Index of Multiple Deprivation (IMD) score Quintiles (most affluent [Q1] to most deprived [Q5]). There was no significant difference in probability of in-hospital mortality in our adjusted model according to sex. The probability of 30-day mortality in our adjusted model was similar between men and women throughout Quintiles, ((Q5; Men 7.6%; 95% CI 7.3–7.8% ( P < .001), Women; 7.0%; 95% CI 6.8–7.3%, P < .001)) ((Q1; Men 7.1%; 95% CI 6.8–7.4%, P < .001, Women; 6.9%; 95% CI 6.6–7.1%, P < .001)). The probability of one-year mortality in our adjusted model was higher in men throughout all Quintiles (Q1; Men 15.0%; 95% CI 14.8–15.6%), P < .001, Women; 14.5%; 95% CI 14.2–14.9%, P < .001) (Q5; Men 16.9%; 95% CI 16.5–17.3%, P < .001, Women; 15.5%; 95% CI 15.1–15.9 by %, P < .001). Overall, female sex did not significantly influence the effect of deprivation on AMI processes of care and outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".