Perioperative predictors for post-prostatectomy urinary incontinence in prostate cancer patients following robotic-assisted radical prostatectomy: Long-term results of a Canadian prospective cohort
Bibliographic record
Abstract
INTRODUCTION: We aimed to report the impact of perioperative factors that have not been well-studied on continence recovery following robotic-assisted radical prostatectomy (RARP). METHODS: We analyzed data of 322 men with localized prostate cancer who underwent RARP between October 2006 and May 2015 in a single Canadian centre. All patients were assessed at one, three, six, 12, and 24 months after surgery. We evaluated risk factors for post-prostatectomy urinary incontinence from a prospectively collected database in multivariate Cox regression analysis. The primary endpoint was continence, defined as 0 pad usage per day. RESULTS: 0-pad continence rates were 126/322 (39%), 187/321 (58%), 222/312 (71%), 238/294 (80%), and 233/257 (91%) at one, three, six, 12, and 24 months, respectively. Bladder neck preservation (hazard ratio [HR] 0.71; 95% confidence interval [CI] 0.5-0.99; p=0.04), and prostate size (HR 0.99; 95% CI 0.98-0.99; p=0.02) were independent predictors of continence recovery after RARP. Smoking at time of surgery predicted delayed continence recovery on multivariate analysis (HR 1.42; 95% CI 1.01-1.99; p=0.04). Neurovascular bundles preservation was associated with continence recovery after 24 months. No statistically significant correlation was found with other variables, such as age, body mass index, Charlson comorbidity index, preoperative oncological baseline parameters, presence of median lobe, or thermal energy use. CONCLUSIONS: Our results confirmed known predictors of postprostatectomy incontinence (PPI), namely bladder neck resection and large prostate volume. Noteworthy, cigarette smoking at the time of RARP was found to be a possible independent risk factor for PPI. This study is hypothesis-generating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".