Decision regret after robot-assisted radical prostatectomy: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: Robot-assisted radical prostatectomy (RARP) is the most commonly performed surgical treatment for prostate cancer. However, decision regret (DR) represents a concern for both patients undergoing the procedure and clinicians involved in therapeutic management. To address this need, we performed a systematic review exploring DR severity and its associations after RARP. Methods: A comprehensive search in scientific literature databases (PubMed, Embase, Scopus, and Web of Science) identified studies on DR in RARP-treated patients. All studies objectively evaluating DR were included. Within studies using the validated 5-item DR scale (range 0-100), the pooled estimate was calculated using fixed- and random-effects models accounting for different follow-ups. A qualitative synthesis analyzed the impact of multiple baseline, perioperative, and postoperative factors on DR. Results: We retrieved 493 articles using our search strategy, with 15 meeting inclusion criteria. A total of 3480 prostate cancer patients with objective DR assessment after RARP were identified. The median follow-up ranged from 4.8 months to 6.3 years while response rates varied between 45% and 100%. Among the included studies, 10 used the Decision Regret Scale, with a pooled mean estimate of 15.22 (95% confidence interval 11.52-18.93) under the random-effects model. In the remaining five studies, DR was generally low (65%-75%) and even absent in some (12%-49%). Functional outcomes, such as continence and potency, were the most frequently reported factors significantly associated with DR. However, variability in assessing DR and other outcomes limits the ability to draw definitive conclusions. Conclusion: Most patients report low DR after RARP. Functional outcomes correlate with DR, but the heterogeneity in assessments and reporting methods warrants the need for more standardized evaluation.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".