Race and Surgical Mortality in the United States
Bibliographic record
Abstract
OBJECTIVE: This study describes racial differences in postoperative mortality following 8 cardiovascular and cancer procedures and assesses possible explanations for these differences. SUMMARY BACKGROUND DATA: Although racial disparities in the use of surgical procedures are well established, relationships between race and operative mortality have not been assessed systematically. METHODS: We used national Medicare data to identify all patients undergoing one of 8 cardiovascular and cancer procedures between 1994 and 1999. We used multiple logistic regression to assess differences in operative mortality (death within 30 days or before discharge) between black patients and white patients, controlling for patient characteristics. Adding hospital indicators to these models, we then assessed the extent to which racial differences in operative mortality could be accounted for by the hospital in which patients were cared for. RESULTS: Black patients had higher crude mortality rates than white patients for 7 of the 8 operations, including coronary artery bypass, aortic valve replacement, abdominal aortic aneurysm repair, carotid endarterectomy, radical cystectomy, pancreatic resection, and esophagectomy. Among these 7 procedures, odds ratios of mortality (black versus white) ranged from 1.23 (95% confidence interval, 1.18-1.29) for CABG to 1.61 (95% confidence interval, 1.28-2.03) for esophagectomy. Adjusting for patient characteristics had modest or no effect on odds ratios of mortality by race. However, there remained few clinically or statistically significant differences in mortality by race after we accounted for hospital. Hospitals that treated a large proportion of black patients had higher mortality rates for all 8 procedures, for white as well as black patients. CONCLUSIONS: Black patients have higher operative mortality risks across a wide range of surgical procedures, in large part because of higher mortality rates at the hospitals they attend.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".