Impact of Age on Long-Term Urinary Continence after Robotic-Assisted Radical Prostatectomy
Bibliographic record
Abstract
Aim and Objectives: We aimed to test the impact of age on long-term urinary continence (≥12 months) in patients undergoing robotic-assisted radical prostatectomy. Methods and Materials: We relied on an institutional tertiary-care database to identify the patients who underwent robotic-assisted radical prostatectomy between January 2014 and January 2021. Patients were divided into three age groups: age group one (≤60 years), age group two (61–69 years) and age group three (≥70 years). Multivariable logistic regression models tested the differences between the age groups in the analyses addressing long-term urinary continence after robotic-assisted radical prostatectomy. Results: Of the 201 prostate cancer patients treated with robotic-assisted radical prostatectomy, 49 (24%) were assigned to age group one (≤60 years), 93 (46%) to age group two (61–69 years) and 59 (29%) to age group three (≥70 years). The three age groups differed according to long-term urinary continence: 90% vs. 84% vs. 69% for, respectively, age group one vs. two vs. three (p = 0.018). In the multivariable logistic regression, age group one (Odds Ratio (OR) 4.73, 95% CI 1.44–18.65, p = 0.015) and 2 (OR 2.94; 95% CI 1.23–7.29; p = 0.017) were independent predictors for urinary continence, compared to age group three. Conclusion: Younger age, especially ≤60 years, was associated with better urinary continence after robotic-assisted radical prostatectomy. This observation is important at the point of patient education and should be discussed in informed consent.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".