Sex-based analysis of NSTEMI processes of care and outcomes by hospital: a nationwide cohort study
Bibliographic record
Abstract
Abstract Background Contemporary studies have demonstrated that in non-ST-segment elevation myocardial infarction (NSTEMI), processes of care vary significantly according to biological sex. Little is known regarding variation in practice between geographical areas and individual centers. Methods & Results We identified 305,014 admissions with NSTEMI in the United Kingdom (UK) Myocardial Infarction National Audit Project (MINAP), 2010-2017, including female sex (n = 110,209). Females presented significantly older (77y vs. 69y, P<0.001), were more likely to be Caucasian (93% vs. 91%, P<0.001) and less likely to be current smokers (18% vs. 24%, P<0.001). Females were less frequently treated with GDMT after NSTEMI, less frequently managed with an invasive coronary angiogram (ICA) (58% vs. 75%, P<0.001) during index admission and less frequently underwent PCI (35% vs. 49%, P<0.001) or CABG surgery (5% vs. 9%, P<0.001) compared to males. Structural process of care differed between the sexes, with a lower proportion of females being treated on a dedicated cardiology ward (48% vs. 56%, P<0.001) or admitted under a attending cardiologist (44% vs. 52%, P<0.001). In our hospital-clustered analysis, we show a positive correlation between the risk-standardized mortality rates (RSMR) and increasing proportion of women treated for NSTEMI (R2=0.17, P<0.001). There was a clear negative correlation between the proportion of females who had an optimum opportunity-based quality indicator score (surrogate for optimum process of care) during their admission and RSMR (R2 =0.22, P<0.001), with a far weaker correlation in males (R2 =0.08, P<0.001). Conclusion There was a significant in variation of the management of patients with NSTEMI according to sex, with widespread geographical variation. Structural changes are likely required to enable successful change for female patients.
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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) | 0.002 | 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".