Readmission and unplanned healthcare use after radical cystectomy are independent of discharge destination
Bibliographic record
Abstract
INTRODUCTION: Our primary purpose was studying utilization rates of home nursing assistance (HNA) and skilled nursing facility (SNF) placement after radical cystectomy (RC) and evaluating if their use was associated with emergency department (ED) visits, hospital readmissions, or mortality. Secondarily, we evaluated if patient socioeconomic status was associated with these factors following RC. METHODS: Patients who underwent RC for bladder cancer were retrospectively analyzed. Discharge destination was labeled as home, HNA, or SNF. The incidence of ED visits was recorded at 30 and 90 days after discharge from surgical admission. Readmissions were tracked similarly. Area deprivation index (ADI) was collected on each patient and organized in quartiles (ADIQ), with worsening socioeconomic status as ADIQ increased. RESULTS: A total of 215 patients were discharged home, 148 to HNA and 25 to SNF. ED visits and readmissions after RC at the 30- and 90-day marks did not differ based on discharge destination (p>0.05). Home patients had a lower incidence of death after RC compared to HNA and SNF (p=0.037), but not overall survival (OS) time (p=0.572). Readmission to the hospital after 30 days of discharge was more likely as ADIQ increased (p=0.017). Discharge destination, ED visits, and readmission after 90 days of discharge from RC were not different based on patient ADIQ (p>0.05). CONCLUSIONS: Discharge to home after RC is associated with lower mortality rates. Rates of readmission and use of ED resources appear independent of discharge destination. A greater ADIQ may interact with the likelihood of admission post-RC. Future efforts remain warranted to address disparities in postoperative management in the pursuit of health equity in urology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".