Relation of Hospital Volume to Colostomy Rates and Survival for Patients With Rectal Cancer
Bibliographic record
Abstract
BACKGROUND: Postoperative mortality after some types of cancer surgery is inversely related to the number of operations performed at a hospital (i.e., hospital volume). This study assessed the association of hospital volume with colostomy rates and survival for patients with rectal cancer in a large representative cohort identified from the California Cancer Registry. METHODS: We identified 7257 patients diagnosed from January 1, 1994, through December 31, 1997, with stage I-III rectal cancer who underwent surgical resection. Registry data were linked to hospital discharge abstracts and ZIP-code-level data from the 1990 U.S. Census. Associations of hospital volume with permanent colostomy and 30-day mortality were assessed with the Mantel-Haenszel trend test and logistic regression. Overall survival was examined with the Kaplan-Meier method and a multivariable Cox proportional hazards model. Multivariable analyses adjusted for demographic and clinical variables and patient clustering within hospitals. All tests of statistical significance were two-sided. RESULTS: In unadjusted analyses across decreasing quartiles of hospital volume, we observed statistically significant increases in colostomy rates (29.5%, 31.8%, 35.2%, and 36.6%; P<.001) and in 30-day postoperative mortality (1.6%, 1.6%, 2.9%, and 4.8%; P<.001) and a decrease in 2-year survival (83.7%, 83.2%, 80.9%, and 76.6%; P<.001). The adjusted risks of permanent colostomy (odds ratio [OR] = 1.37, 95% confidence interval [CI] = 1.10 to 1.70), 30-day mortality (OR = 2.64, 95% CI = 1.41 to 4.93), and 2-year mortality (hazard ratio = 1.28, 95% CI = 1.15 to 1.44) were greater for patients at hospitals in the lowest volume quartile than for patients at hospitals in the highest volume quartile. Stratification by tumor stage and comorbidity index did not appreciably affect the results. Adjusted colostomy rates varied statistically significantly (P<.001) among individual hospitals independent of volume. CONCLUSIONS: Rectal cancer patients who underwent surgery at high-volume hospitals were less likely to have a permanent colostomy and had better survival rates than those treated in low-volume hospitals. Identifying processes of care that contribute to these differences may improve patients' outcomes in all hospitals.
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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 | 0.003 |
| 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) | 0.001 | 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".