Examining Causes of Racial Disparities in General Surgical Mortality
Bibliographic record
Abstract
BACKGROUND: Racial disparities in general surgical outcomes are known to exist but not well understood. OBJECTIVES: To determine if black-white disparities in general surgery mortality for Medicare patients are attributable to poorer health status among blacks on admission or differences in the quality of care provided by the admitting hospitals. RESEARCH DESIGN: Matched cohort study using Tapered Multivariate Matching. SUBJECTS: All black elderly Medicare general surgical patients (N=18,861) and white-matched controls within the same 6 states or within the same 838 hospitals. MEASURES: Thirty-day mortality (primary); others include in-hospital mortality, failure-to-rescue, complications, length of stay, and readmissions. RESULTS: Matching on age, sex, year, state, and the exact same procedure, blacks had higher 30-day mortality (4.0% vs. 3.5%, P<0.01), in-hospital mortality (3.9% vs. 2.9%, P<0.0001), in-hospital complications (64.3% vs. 56.8% P<0.0001), and failure-to-rescue rates (6.1% vs. 5.1%, P<0.001), longer length of stay (7.2 vs. 5.8 d, P<0.0001), and more 30-day readmissions (15.0% vs. 12.5%, P<0.0001). Adding preoperative risk factors to the above match, there was no significant difference in mortality or failure-to-rescue, and all other outcome differences were small. Blacks matched to whites in the same hospital displayed no significant differences in mortality, failure-to-rescue, or readmissions. CONCLUSIONS: Black and white Medicare patients undergoing the same procedures with closely matched risk factors displayed similar mortality, suggesting that racial disparities in general surgical mortality are not because of differences in hospital quality. To reduce the observed disparities in surgical outcomes, the poorer health of blacks on presentation for surgery must be addressed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".