Association of Physician and Hospital Volume With Use of Aspirin and Reperfusion Therapy in Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: The association between volume of patients treated and quality of care has important implications for patient referral policies and approaches to quality improvement. Most studies have focused on hospital volume alone and health outcomes. OBJECTIVES: The objective of this work was to examine the association of hospital and physician volume with use of aspirin and reperfusion therapy in the management of acute myocardial infarction (AMI) in eligible patients. METHODS: We reviewed charts of 2,215 patients treated at 35 Minnesota hospitals for AMI between October 1, 1992, and July 31, 1993, comparing use of aspirin and reperfusion therapy in eligible patients across different physician and hospital volume categories through multiple logistic regression. RESULTS: Aspirin use did not vary significantly with physician volume. Use of reperfusion therapy was reduced among the lowest-volume physicians only (adjusted OR, 0.38; 95% CI, 0.15-0.94). Compared with the highest volume hospitals (treating >200 patients), aspirin use among lower-volume hospitals was lower. This was statistically significant only in the hospitals treating <30 patients (adjusted OR, 0.54; 95% CI, 0.30-0.97). These same hospitals had increased odds of using thrombolytics (adjusted OR, 3.02; 95% CI, 1.40-6.53). CONCLUSIONS: Differences in use of aspirin and reperfusion therapy occur at the extremes of hospital and physician volume. These observed differences are in the anticipated direction, except for the increased use of thrombolytics at very-low-volume hospitals. This may be a "desperation reaction" with a perceived lack of other alternatives, such as cardiac catheterization labs and cardiologists.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".