Predictors of Immediate Continence Following Robot-Assisted Radical Prostatectomy
Bibliographic record
Abstract
INTRODUCTION: Few studies have examined the patient characteristics that lead to early continence after robot-assisted radical prostatectomy (RARP), and to date, there has been no investigation into the predictors of immediate continence. In the current study, we examine a large multisurgeon population of patients undergoing RARP to assess for predictors of this outcome. PATIENTS AND METHODS: Between January 2008 and December 2010, 1270 patients who underwent RARP at our institution, with complete preoperative and follow-up data, were assessed for urinary function prospectively. Univariable and multivariable logistic regressions were used to assess for predictors of zero pad usage after RARP. Patient and operative characteristics examined include age, body-mass index, prostate-specific antigen, adjusted Charlson comorbidity index (CCI), Gleason sum, international prostate symptom score, clinical stage, nerve sparing, bladder neck reconstruction, posterior anastomotic reconstruction, surgeon volume, and percutaneous suprapubic tube (PST) bladder drainage. RESULTS: Overall, 17.3% of patients (n=219) never required a pad after catheter removal. Characteristics associated with never requiring a pad are age, preoperative Gleason sum, CCI, nerve sparing, prostate weight, surgeon volume, and PST bladder drainage. Independent predictors of never requiring a pad after catheter removal included nerve-sparing (B/L standard as referent) wide dissection [OR: 0.96 (95% CI: 0.49, 1.88)], unilateral inter-/intrafascial [OR: 1.20 (0.70, 2.06)], bilateral inter-/intrafascial [OR: 1.97 (1.36, 2.86)], and PST drainage [OR: 2.53 (1.56, 4.11)]. CONCLUSION: In a study reflective of broad RARP practice at our institution, 17.3% of patients were entirely pad free after RARP. The type of nerve sparing performed and placement of a PST for bladder drainage postoperatively were found to be independently predictive of never requiring a pad after RARP.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".