Does surgical delay for radical prostatectomy affect patient pathological outcome? A retrospective analysis from a Canadian cohort
Bibliographic record
Abstract
INTRODUCTION: We sought to assess the impact of surgical wait time (SWT) to robot-assisted radical prostatectomy (RARP) on final pathological outcome. METHODS: A retrospective review of RARP patient records operated between 2006 and 2015 was conducted. SWT was defined as period from prostate biopsy to surgery. Primary outcome was the impact on postoperative Cancer of the Prostate Risk Assessment (CAPRA-S) score. Patients were stratified according to D'Amico risk categories. Univariate analysis (UVA) and multivariable (MVA) analysis with a generalized linear model was used to evaluate the effect of SWT and other predictive factors on pathological outcome in individual risk group and on the overall sample. RESULTS: A total of 835 patients were eligible for analysis. Mean SWT was significantly different between the three D'Amico groups, with mean SWT of 180.22 days (95% confidence interval [CI] 169.03; 191.41), 159.14 days (95% CI 152.38; 165.90), and 138.96 days (95% CI 124.60; 153.33) for low-, intermediate-, and high-risk groups, respectively (p<0.001). After stratification by D'Amico risk group, no significant association was observed between SWT and CAPRA-S score in the three risk categories on UVA and MVA. Predictors of higher CAPRA-S score in the multivariable model in the overall cohort were: older age (p=0.014), biopsy Gleason score (p<0.001), percentage of positive cores (p<0.001), and clinical stage (p<0.001). CONCLUSIONS: In the present study evaluating SWT for RARP in a Canadian socialized system, increased delay for surgery does not appear to impact the pathological outcome. Further studies are required to evaluate the impact of wait time on biochemical recurrence-free survival, cancer-specific survival, and overall survival.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".