Benefit in regionalisation of care for patients treated with radical cystectomy: a nationwide inpatient sample analysis
Bibliographic record
Abstract
OBJECTIVE: To quantify in absolute terms the potential benefit of regionalisation of care from low- to high-volume hospitals. PATIENTS AND METHODS: Patients with a primary diagnosis of bladder cancer treated with radical cystectomy (RC) were identified within the Nationwide Inpatient Sample, a retrospective observational population-based cohort of the USA, between 1998 and 2009. Intraoperative and postoperative complications, blood transfusions, prolonged length of stay, and in-hospital mortality rates represented the outcomes of interest. Potentially avoidable outcomes were calculated by subtracting predicted rates (i.e. estimated outcomes if care was delivered at a high-volume hospital) from observed rates (i.e. actual observed outcomes after care delivered at a low-volume hospital). Multivariable logistic regression models and number needed to treat were generated. RESULTS: Patients treated at high-volume hospitals had lower odds of complications during hospitalisation than those treated in low-volume hospitals. Potentially avoidable intraoperative complications, postoperative complications, blood transfusions, prolonged hospitalisation, and in-hospital mortality rates were 0.6, 7.4, 2.8, 9.4, and 2.0%, respectively. This corresponds to a number needed to redirect from low- to high-volume hospitals in order to avoid one adverse event of 166, 14, 36, 11 and 50, respectively. CONCLUSION: This is the first report to quantify the potential benefit of regionalisation of RC for muscle-invasive bladder cancer to high-volume 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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".