Geographic variation in the treatment of non-ST-segment myocardial infarction in the English National Health Service: a cohort study
Bibliographic record
Abstract
OBJECTIVES: To investigate geographic variation in guideline-indicated treatments for non-ST-elevation myocardial infarction (NSTEMI) in the English National Health Service (NHS). DESIGN: Cohort study using registry data from the Myocardial Ischaemia National Audit Project. SETTING: All Clinical Commissioning Groups (CCGs) (n=211) in the English NHS. PARTICIPANTS: 357 228 patients with NSTEMI between 1 January 2003 and 30 June 2013. MAIN OUTCOME MEASURE: Proportion of eligible NSTEMI who received all eligible guideline-indicated treatments (optimal care) according to the date of guideline publication. RESULTS: The proportion of NSTEMI who received optimal care was low (48 257/357 228; 13.5%) and varied between CCGs (median 12.8%, IQR 0.7-18.1%). The greatest geographic variation was for aldosterone antagonists (16.7%, 0.0-40.0%) and least for use of an ECG (96.7%, 92.5-98.7%). The highest rates of care were for acute aspirin (median 92.8%, IQR 88.6-97.1%), and aspirin (90.1%, 85.1-93.3%) and statins (86.4%, 82.3-91.2%) at hospital discharge. The lowest rates were for smoking cessation advice (median 11.6%, IQR 8.7-16.6%), dietary advice (32.4%, 23.9-41.7%) and the prescription of P2Y12 inhibitors (39.7%, 32.4-46.9%). After adjustment for case mix, nearly all (99.6%) of the variation was due to between-hospital differences (median 64.7%, IQR 57.4-70.0%; between-hospital variance: 1.92, 95% CI 1.51 to 2.44; interclass correlation 0.996, 95% CI 0.976 to 0.999). CONCLUSIONS: Across the English NHS, the optimal use of guideline-indicated treatments for NSTEMI was low. Variation in the use of specific treatments for NSTEMI was mostly explained by between-hospital differences in care. Performance-based commissioning may increase the use of NSTEMI treatments and, therefore, reduce premature cardiovascular deaths. TRIAL REGISTRATION NUMBER: NCT02436187.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".