First Canadian experience with same-day discharge after robot-assisted radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate the feasibility and safety of implementing a sameday discharge (SD ) protocol for robot-assisted radical prostatectomy (RARP) and pelvic lymph node dissection. METHODS: We performed a prospective cohort study including all consecutive eligible patients undergoing RARP in 2021 following initiation of SDD RARP protocol in April. Baseline characteristics were compared using t-tests, Mann-Whitney U tests, and odds ratios (OR ) calculated using multiple logistic regression to assess for predictors of SD success. RESULTS: A total of 117 patients underwent RARP in 2021 following initiation of the SDD protocol. Fifty-seven patients were initiated on the SD pathway and 60 patients underwent surgery as an inpatient (IP-RARP). Of those on the SD pathway (SD-RARP), 33 (58%) were successfully discharged the same day of surgery, while 24 (42%) failed SD . Baseline demographics were well-balanced between cohorts. Case order, increased patient age, and distance travelled to the hospital were factors associated with selection of patients for the IP-RARP protocol. In total, 12 SD and 12 IP patients presented to the emergency department (p=1.0), and none within 24 hours of discharge. There were no hospital admissions in the SD cohort, with four readmissions in the IP cohort (p=0.1). Multiple logistic regression revealed that case order (first case) was the only predictive factor for SD success (OR 4.08, 95% confidence interval 1.59-11.62, p=0.005). CONCLUSIONS: Implementation of an SD pathway following RARP is feasible, with no increase in rates of complications, unscheduled visits, or readmissions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".