Re-operation within 30 days of radical cystectomy: Identifying high-risk patients and complications using ACS-NSQIP database
Bibliographic record
Abstract
INTRODUCTION: Radical cystectomy (RC) is a highly morbid procedure, with 30-day complication rates approaching 31%. Our objective was to determine risk factors for re-operation within 30 days following a RC for non-metastatic bladder cancer. METHODS: We included all patients who underwent a RC for non-metastatic bladder cancer using The American College of Surgeons National Surgical Quality Improvement Program database between January 1, 2007 and December 31, 2014. Logistic regression analyses were used to evaluate predictors of re-operation. RESULTS: A total of 2608 patients were included; 5.8% of patients underwent re-operation within 30 days. On multivariable analysis, increasing body mass index (BMI) (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.01-1.07), African American race (vs. Caucasian OR 2.29, 95% CI 1.21-4.34), and history of chronic obstructive pulmonary disease (COPD) (OR 2.33, 95% CI 1.45-3.74) were significant predictors of re-operation within 30 days of RC. Urinary diversion type (ileal conduit vs. continent) and history of chemotherapy or radiotherapy within 30 days prior to RC were not. Patients who underwent re-operation within this timeframe had a significantly higher mortality rate (4.0% vs. 1.6%) and were more likely to experience cardiac (7.2% vs. 1.9%), pulmonary (23.0% vs. 3.0%), neurological (2.0% vs. 0.49%), and venous thromboembolic events (10.5% vs. 5.4%), as well as infectious complications (64.5% vs. 24.1%), with a significantly longer hospital length of stay (16.5 vs. 7.0 days). CONCLUSIONS: Recognizing increasing BMI, COPD, and African American race as risk factors for re-operation within 30 days of RC will allow urologists to preoperatively identify such high-risk patients and prompt them to adopt more aggressive approaches to minimize postoperative surgical complications.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".