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 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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".