Association of quality of care and long-term mortality risk for individuals presenting with ST-segment myocardial infarction (STEMI) by diabetes mellitus status: a nationwide cohort study
Bibliographic record
Abstract
Abstract Aims This study aimed to assess how diabetes influences the quality of care and longer-term outcomes in contemporary STEMI cohorts. Methods We analysed 283,658 adults hospitalised with STEMI from the United Kingdom Myocardial Ischaemia National Audit Project (MINAP) registry between 2005 and 2019. This was linked with Office of National Statistics data to provide out of hospital mortality outcomes. We compared longer-term outcomes depending on diabetes status and assessed the effect of quality of care using the opportunity-based quality-indicator score (OBQI). Results Individuals with diabetes were older (median age 68.7 vs. 65.5), underwent percutaneous coronary intervention less frequently (60% vs. 63%) and were less likely to achieve a door-to-balloon time of <60 minutes (69% vs. 75%) or <120 minutes (89% vs. 92%). Their adjusted all-cause mortality risk was higher during follow-up, from 30 days (HR: 1.49, CI: 1.44-1.54), to up to 10 years of follow up (HR: 1.54, CI: 1.52-1.57), compared to individuals without diabetes. Excellent inpatient care was associated with lower mortality rates within individuals with diabetes (Diabetes: HR 0.56, CI: 0.50-0.64, No diabetes: HR 0.62, CI: 0.58-0.67). Conclusions Individuals with diabetes have a higher risk of long-term mortality after STEMI. They experience delays in angiography and receive lower quality inpatient care.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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".