Racial Disparities in Operative Outcomes After Major Cancer Surgery in the United States
Bibliographic record
Abstract
BACKGROUND: Numerous studies have recorded racial disparities in access to care for major cancers. We investigate contemporary national disparities in the quality of perioperative surgical oncological care using a nationally representative sample of American patients and hypothesize that disparities in the quality of surgical oncological care also exists. METHODS: A retrospective, serial, and cross-sectional analysis of a nationally representative cohort of 3,024,927 patients, undergoing major surgical oncological procedures (colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, pneumonectomy, pancreatectomy, and prostatectomy), between 1999 and 2009. RESULTS: After controlling for multiple factors (including socioeconomic status), Black patients undergoing major surgical oncological procedures were more likely to experience postoperative complications (OR: 1.24; p < 0.001), in-hospital mortality (OR: 1.24; p < 0.001), homologous blood transfusions (OR: 1.52; p < 0.001), and prolonged hospital stay (OR: 1.53; p < 0.001). Specifically, Black patients have higher rates of vascular (OR: 1.24; p < 0.001), wound (OR: 1.10; p = 0.004), gastrointestinal (OR: 1.38; p < 0.001), and infectious complications (OR: 1.29; p < 0.001). Disparities in operative outcomes were particularly remarkable for Black patients undergoing colectomy, prostatectomy, and hysterectomy. Importantly, substantial attenuation of racial disparities was noted for radical cystectomy, lung resection, and pancreatectomy relative to earlier reports. Finally, Hispanic patients experienced no disparities relative to White patients in terms of in-hospital mortality or overall postoperative complications for any of the eight procedures studied. CONCLUSIONS: Considerable racial disparities in operative outcomes exist in the United States for Black patients undergoing major surgical oncological procedures. These findings should direct future health policy efforts in the allocation of resources for the amelioration of persistent disparities in specific procedures.
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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 | 0.000 | 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) | 0.001 | 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".