Outpatient <i>vs</i> Inpatient Robot-Assisted Radical Prostatectomy: An Evidence-Based Analysis of Comparative Outcomes
Bibliographic record
Abstract
Purpose: To provide a systematic analysis of outcomes comparing outpatient and inpatient robot-assisted radical prostatectomy (RARP) for prostate cancer based on the best available evidence. Methods: A comprehensive search of electronic databases (PubMed, Web of Science, Scopus, and Cochrane Library) was conducted to determine eligible comparative studies as of July 2021. The Newcastle-Ottawa scale was used to assess the quality of the included studies. Parameters including perioperative, oncologic, and functional outcomes were collected. Results: Nine studies with 2721 patients were included, of which 831 underwent outpatient RARP and 1890 underwent inpatient RARP. The combined results demonstrated that compared with the inpatient group, the outpatient group had shorter operation time (weighted mean difference −8.59, 95% confidence interval [CI] −14.08 to −3.10, p = 0.002) and lower overall complication rate (odds ratio 0.64, 95% CI 0.44 to 0.95, p = 0.03). However, there were no significant differences regarding estimated blood loss, readmission rate, positive surgical margin, and urinary continence rates between the groups. Conclusions: Outpatient RARP does not increase the incidence of complications and readmissions compared with inpatient RARP. This suggests that routine same-day discharge after providing patients with RARP is safe and feasible.
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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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".