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Record W2072989483 · doi:10.5489/cuaj.2086

Predictors of early continence following robot-assisted radical prostatectomy

2015· article· en· W2072989483 on OpenAlexaffvenue
Hugo Lavigueur‐Blouin, Alina Camacho Noriega, Roger Valdivieso, Pierre‐Alain Hueber, Marc Bienz, Naif Alhathal, Mathieu Latour, Assaad El‐Hakim, Kevin C. Zorn

Bibliographic record

VenueCanadian Urological Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHôpital du Sacré-Cœur de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineProstatectomyUrinary continenceInternational Prostate Symptom ScoreShim (computing)Body mass indexLogistic regressionOdds ratioConfidence intervalUrinary incontinenceUnivariate analysisUrologyMultivariate analysisProstate cancerSurgeryProstateLower urinary tract symptomsErectile dysfunctionInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Functional outcomes after robot-assisted radical prostatectomy (RARP) greatly influence patient quality of life. Data regarding predictors of early continence, especially 1 month following RARP, are limited. Previous reports mainly address immediate or 3-month postoperative continence rates. We examine preoperative predictors of pad-free continence recovery at the first follow-up visit 1 month after RARP. METHODS: Between January 2007 and January 2013, preoperative and follow-up data were prospectively collected for 327 RARP patients operated on by 2 fellowship-trained surgeons (AEH and KCZ). Patient and operative characteristics included age, body mass index (BMI), staging, preoperative prostate-specific antigen (PSA), prostate weight, International Prostate Symptom Score (IPSS), Sexual Health Inventory for Men (SHIM) score and type of nerve-sparing performed. Continence was defined by 0-pad usage at 1 month follow-up. Univariate and multivariate logistic regression models were used to assess for predictors of early continence. RESULTS: Overall, 44% of patients were pad-free 1 month post-RARP. In multivariate regression analysis, age (odds ratio [OR] 0.946, confidence interval [CI] 95%: 0.91, 0.98) and IPSS (OR: 0.953, CI 95%: 0.92, 0.99) were independent predictors of urinary continence 1 month following RARP. Other variables (BMI, staging, preoperative PSA, SHIM score, prostate weight and type of nerve-sparing) were not statistically significant predictors of early continence. Limitations of this study include missing data for comorbidities, patient use of pelvic floor exercises and patient maximal activity. Moreover, patient-reported continence using a 0-pad usage definition represents a semiquantitative and subjective measurement. CONCLUSION: In a broad population of patients who underwent RARP at our institution, 44% of patients were pad-free at 1 month. Age and IPSS were independent predictors of early continence after surgery. Men of advanced age and those with significant lower urinary tract symptoms prior to RARP should be counselled on the increased risk of urinary incontinence in the early stages.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.249
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations78
Published2015
Admission routes2
Has abstractyes

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