Association of admitting physician specialty and care quality and outcomes in non-ST-segment elevation myocardial infarction (NSTEMI): insights from a national registry
Bibliographic record
Abstract
AIM: Little is known about the association between admitting physician specialty and care quality and outcomes for non-ST-segment elevation myocardial infarction (NSTEMI). METHODS AND RESULTS: We identified 288 420 patients hospitalized with NSTEMI between 2010 and 2017 in the UK Myocardial Infarction National Audit Project database. The cohort was dichotomized according to care under a non-cardiologist (n = 146 722) and care under a cardiologist (n = 141 698) within the first 24 h of admission to hospital. Patients admitted under a cardiologist were significantly younger (70 vs. 75 years, P < 0.001), and less likely to be female (32% vs. 39%, P < 0.001). Independent factors associated with admission under a cardiologist included prior history of percutaneous coronary intervention (PCI) [odds ratio (OR) 1.04, 95% confidence interval (CI) 1.01-1.07; P = 0.04], hypercholesterolaemia (OR 1.17, 95% CI 1.15-1.20; P < 0.001), hypertension (OR 1.03, 95% CI 1.01-1.04; P = 0.01), and admission to an interventional centre (OR 3.90, 95% CI 3.79-4.00; P < 0.001). Patients admitted under cardiology were more likely to receive optimal pharmacotherapy, undergo invasive coronary angiography (79% vs. 60%, P < 0.001), and receive revascularization in the form of PCI (52% vs. 36%, P < 0.001). Following propensity score matching, odds of in-hospital all-cause mortality (OR 0.81, 95% CI 0.79-0.85; P < 0.001), re-infarction (OR 0.78, 95% CI 0.66-0.91; P = 0.001), and major adverse cardiovascular events (OR 0.81, 95% CI 0.78-0.84; P < 0.001) were lower in patients admitted under a cardiologist. CONCLUSION: Patients with NSTEMI admitted under a cardiologist within 24 h of hospital 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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".