Relationship Between Annual Volume of Patients Treated by Admitting Physician and Mortality After Acute Myocardial Infarction
Bibliographic record
Abstract
CONTEXT: Acute myocardial infarction (AMI) is a common condition that is treated by physicians with varying levels of clinical experience, but whether the level of experience affects outcome remains uncertain. OBJECTIVE: To evaluate the relationship between the average annual volume of cases treated by admitting physicians and mortality after AMI. DESIGN, SETTING, AND PATIENTS: Retrospective cohort study using linked administrative databases containing patient admission information for 98 194 patients treated by 5374 physicians between April 1, 1992, and March 31, 1998, in Ontario, Canada. MAIN OUTCOME MEASURES: Mortality risk rates for 30 days and 1 year post-AMI, adjusted by physician volume and patient, physician, and hospital characteristics. RESULTS: The 30-day mortality rate was 13.5% and the 1-year mortality rate was 21.8%. A strong inverse relationship between the average annual volume of AMI cases treated by the admitting physician and mortality after an AMI was observed. The 30-day risk-adjusted mortality rate was 15.3% for physicians who treated 5 or fewer AMI cases per year (lowest quartile) compared with 11.8% for physicians who treated more than 24 AMI cases annually (highest quartile; P<.001). The 1-year risk-adjusted mortality rate was 24.2% for physicians who treated 5 or fewer AMI cases per year (lowest quartile) compared with 19.6% for physicians who treated more than 24 AMI cases annually (highest quartile; P<.001). CONCLUSION: Patients with AMI who are treated by high-volume admitting physicians are more likely to survive at 30 days and 1 year.
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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 | 0.001 | 0.006 |
| 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) | 0.002 | 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".