Association of admitting physician specialty and care quality and outcomes in non-ST-segment elevation myocardial infarction (NSTEMI): insights from a national registry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it