30-day readmission after radical cystectomy: Identifying targets for improvement using the phases of surgical care
Bibliographic record
Abstract
INTRODUCTION: Postoperative readmissions following radical cystectomy (RC) have gained attention in the past decade. Postoperative and post-discharge complications play a role in readmission rates; however, our ability to predict readmissions remains poor. METHODS: Using the National Surgical Quality Improvement Program database, we identified patients with bladder cancer undergoing RC from 2013-2015. Complications were defined as postoperative and post-discharge. Outcomes were 30-day readmission, post-discharge complications, and post-discharge major complications. Patient, operative, and complication factors were assessed using multivariable logistic regression. RESULTS: We identified 4457 patients who underwent RC; 9.2% of patients experienced a postoperative complication, 18.8% experienced a post-discharge complication, and 20.3% were readmitted. Overweight and obese body mass index (BMI), dependent functional status, chronic obstructive pulmonary disease (COPD), a continent diversion, and duration of operation were associated with post-discharge complications. Postoperative complications were not associated with post-discharge complications. Readmission was associated with Black race (odds ratio [OR] 1.5; 95% confidence interval [CI] 1.0-2.1), overweight (OR 1.5; 95% CI 1.2-1.8) and obese BMI (OR 1.5; 95% CI 1.2-1.9), diabetes (OR 1.2; 95% CI 1.0-1.5), COPD (OR 1.4; 95% CI 1.0-1.8), steroid use (OR 1.5; 95% CI 1.0-2.2), a continent diversion (OR 1.4; 95% CI 1.1-1.7), duration of operation (OR 1.1; 95% CI 1.1-1.2), and postoperative complications (OR 1.5; 95% CI 1.2-2.0). The majority of readmissions experienced a post-discharge complication. CONCLUSIONS: Factors that span the preoperative, intraoperative, postoperative, and post-discharge phases of care were identified to increase readmission risk. To improve readmission rates, interventions will have to target factors across the surgical experience.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".