Few modifiable factors predict readmission following radical cystectomy
Bibliographic record
Abstract
INTRODUCTION: We sought to determine the patient and provider-related factors associated with readmission after radical cystectomy (RC) for bladder cancer. In this era of healthcare reform, hospital performance measures, such as readmission, are beginning to affect provider reimbursement. Given its high readmission rate, RC could be a target for quality improvement. METHODS: We reviewed bladder cancer patients who underwent RC in California's State Inpatient Database (2005-2009) of the Healthcare Cost and Utilization Project. We examined patient-(e.g., race, discharge disposition) and provider-related factors (e.g., volume) and evaluated their association with 30-day readmission. Multivariable logistic regression was used to examine associations of interest. RESULTS: Overall, 22.8% (n = 833) of the 3649 patients who underwent RC were readmitted within 30 days. Regarding disposition, 34.8%, 50.8%, and 12.2% were discharged home, home with home healthcare, and to a post-acute care facility (PACF), respectively. Within 30 days, 20.3%, 20.9%, and 42.3% were discharged home, home with home healthcare, and to a PACF were readmitted, respectively. African Americans (odds ratio [OR] 1.64, 95% confidence interval [CI] 1.07-2.50), having ≥2 comorbidities (OR 1.42, 95% CI 1.06-1.91), receiving a neobladder (OR 1.45, 95% CI 1.09-1.93), and discharged to a PACF (OR 3.79, 95% CI 2.88-4.98) were independent factors associated with readmission. Hospital stays ≥15 days were associated with less readmission (OR 0.43, 95% CI 0.27-0.67, p = 0.0002). Procedure volume was not associated with complication, in-hospital mortality, or readmission. CONCLUSIONS: About one-fifth of patients undergoing RC are readmitted. Patients who are discharged to a PACF, African American, and who have more extensive comorbidities tend to experience more readmissions. Increased efforts with care coordination among these patients may help reduce readmissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".