Impact of the admitting ward on care quality and outcomes in non-ST-segment elevation myocardial infarction: insights from a national registry
Bibliographic record
Abstract
AIMS: Little is known about the association between the type of admission ward and quality of care and outcomes for non-ST-segment elevation myocardial infarction (NSTEMI). METHODS AND RESULTS: We analysed data from 337 155 NSTEMI admissions between 2010 and 2017 in the UK Myocardial Ischaemia National Audit Project (MINAP) database. The cohort was dichotomised according to receipt of care either on a medical (n = 142,876) or cardiac ward, inclusive of acute cardiac wards and cardiac care unit (n = 194,279) on admission to hospital. Patients admitted to a cardiac ward were younger (median age 70 y vs. 75 y, P < 0.001), and less likely to be female (33% vs. 40%, P < 0.001). Independent factors associated with admission to a cardiac ward included ischaemic ECG changes (OR: 1.20, 95% CI: 1.18-1.23) and prior percutaneous coronary intervention (PCI) (OR: 1.19, 95% CI: 1.16-1.22). Patients admitted to a cardiac ward were more likely to receive optimal pharmacotherapy with statin (85% vs. 81%, P < 0.001) and dual antiplatelet therapy (DAPT) (91% vs. 88%, P < 0.001) on discharge, undergo invasive coronary angiography (78% vs. 59%, P < 0.001), and receive revascularisation in the form of PCI (52% vs. 36%, P < 0.001). Following multivariable logistic regression, the odds of inhospital all-cause mortality (OR: 0.75, 95% CI: 0.70-0.81) and major adverse cardiovascular events (MACE) (OR: 0.84, 95% CI: 0.78-0.91) were lower in patients admitted to a cardiac ward. CONCLUSION: Patients with NSTEMI admitted to a cardiac ward on admission were more likely to receive guideline directed management and had better clinical 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".