Predictors of failed same-day discharge in patients undergoing robot-assisted radical prostatectomy in a Canadian universal healthcare system
Bibliographic record
Abstract
INTRODUCTION: Same-day discharge (SDD) after robot-assisted radical prostatectomy (RARP) has been shown to be feasible and safe. In order to improve uptake of this ambulatory model in Canada, we aimed to update our experience of SDD after RARP and identify reasons for SDD pathway non-initiation and failure in a universal healthcare system. METHODS: A review of our prospectively collected database of patients undergoing RARP at a Canadian tertiary academic center from May 2021 to May 2023 was conducted. Binary logistic regression analysis determined predictors SDD pathway non-initiation and failure. RESULTS: We identified 387 patients, of which 198 were initiated on the SDD pathway. Of those initiated, 104 (52.5%) were successfully discharged home on the same day. Patients who travelled distances greater than 100 km, or who had non-CPAP (continuous positive airway pressure)-compliant obstructive sleep apnea were significantly less likely to be initiated on the SDD pathway (both p<0.05). Patients who were scheduled to be the second case or later had an estimated blood loss ≥300 mL, or had a postoperative abdominal drain, were predictive of failing SDD after initiation (all p<0.05). There were similar rates of readmissions, unscheduled office visits, and emergency department presentations, when compared to the traditional inpatient model (all p>0.05). CONCLUSIONS: SDD after RARP in a Canadian healthcare system remains feasible and safe for selected patients. Predictors of failed SDD identified in this study inform the development of future ambulatory protocols and highlight areas of need in infrastructure to increase uptake of these outpatient pathways.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".