Sex-based analysis of NSTEMI processes of care and outcomes by hospital: a nationwide cohort study
Bibliographic record
Abstract
BACKGROUND: Contemporary studies demonstrate that non-ST-segment elevation myocardial infarction (NSTEMI) processes of care vary according to sex. Little is known regarding variation in practice between geographical areas and centres. METHODS: We identified 305 014 NSTEMI admissions in the United Kingdom (UK) Myocardial Ischaemia National Audit Project (MINAP), 2010-17, including female sex (110 209). Hierarchical, multivariate logistic regression models were fitted, assessing for differences in primary outcomes according to sex. Risk-standardized mortality rates (RSMR) were calculated for individual hospitals to illustrate the correlation with variables of interest. 'Heat maps' were plotted to show regional and sex-based variation in the opportunity-based quality indicator score (surrogate for optimal processes of care). RESULTS: Women presented older (77 years vs. 69 years, P < 0.001) and were more often Caucasian (93% vs. 91%, P < 0.001). Women were less frequently managed with an invasive coronary angiogram (58% vs. 75%, P < 0.001) or percutaneous coronary intervention (35% vs. 49%, P < 0.001). In our hospital-clustered analysis, we show a positive correlation between the RSMR and the increasing proportion of women treated for NSTEMI (R2 = 0.17, P < 0.001). There was a clear negative correlation between the proportion of women who had an optimum OBQI score during their admission and RSMR (R2 = 0.22, P < 0.001), with a weaker correlation in men (R2 = 0.08, P < 0.001). Heat maps according to the Clinical Commissioning Group (CCG) demonstrate significant regional variation in the OBQI score, with women receiving poorer quality care throughout the UK. CONCLUSION: There was a significant variation in the management of patients with NSTEMI according to sex, with widespread geographical variation. Structural changes are required to enable improved care for women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".