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MP03-06 POST-VOID DRIBBLING AFTER URETHROPLASTY: INCIDENCE AND ASSOCIATIONS

2021· article· en· W3192486746 on OpenAlexaboutno aff
Jordan Bekkema, Keith Rourke

Bibliographic record

VenueThe Journal of Urology · 2021
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineUrethroplastyUrethral strictureIncidence (geometry)SurgeryLogistic regressionUrinationUrethraInternal medicineUrinary system

Abstract

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You have accessJournal of UrologyTrauma/Reconstruction/Diversion: Urethral Reconstruction (including Stricture, Diverticulum) I (MP03)1 Sep 2021MP03-06 POST-VOID DRIBBLING AFTER URETHROPLASTY: INCIDENCE AND ASSOCIATIONS Jordan Bekkema, and Keith Rourke Jordan BekkemaJordan Bekkema More articles by this author , and Keith RourkeKeith Rourke More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001964.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Post-void dribbling (PVD) is a potential consequence of urethroplasty. The incidence, cause and impact of this symptom remains unclear. Our objective is to examine the impact of urethroplasty on PVD and factors associated with de novo PVD. METHODS: From 2011-2018, patients were enrolled in a prospective single-center study assessing patient-reported outcomes after urethroplasty. PVD was assessed using a 5-point scale responding to, “After urinating, do you have post-urination dribbling or leakage of urine?” Answers included “Never” (1), “Occasionally” (2), “Sometimes” (3), “Most of the Time” (4), or “All of the Time” (5). Patients were assessed pre-operatively and 6 months post-operatively. Clinically significant PVD was considered a response of 3-5. Wilcoxon signed-rank test was used to compare pre- and post-operative incidence of PVD. Multivariate binary logistic regression was used to determine the association between de novo PVD and clinical factors. RESULTS: 384 patients completed pre- and post-operative questionnaires, mean age was 49.5 years, mean stricture length was 4.5cm. Stricture location was bulbar (59.4%), penile (19.5%), posterior (13.8%), and pan-urethral (7.3%). Stricture etiology included idiopathic (40.1%), iatrogenic (14.1%), traumatic (12.2%) or lichen sclerosus (12.5%). Urethroplasty techniques included buccal mucosa graft onlay (51.8%), anastomotic (30.7%) or staged (12.0%). Pre-operatively 46.9% (180) of patients reported PVD compared to 39.8% (153) post-operatively (p=0.01). Compared to pre-operative status, 25.0% (96) of patients reported improved PVD and 57.0% (219) reported no change. 18.0% (67) of patients experienced de novo PVD. On multivariate binary logistic regression, urethroplasty technique was associated with de novo PVD (p=0.05). Patients undergoing anastomotic urethroplasty were less likely to report de novo PVD (O.R.0.33, 95%CI 0.13-0.83; p=0.02) compared to onlay or staged techniques. No other factor was associated with de novo PVD including age (p=0.59), stricture length (p=0.71), location (p=0.50), etiology (p=0.59), failed endoscopic treatment (p=0.18), previous urethroplasty (p=0.55) or stricture recurrence (p=0.78). De novo PVD was not associated with patient dissatisfaction (10.1% versus 7.6%; p=0.49). CONCLUSIONS: PVD is common in patients with urethral stricture and there is an overall improvement after urethroplasty. 18.0% of patients will experience de novo PVD. The impact on patient dissatisfaction is unclear, patients undergoing anastomotic urethroplasty are less likely to experience de novo PVD. Source of Funding: University of Alberta Department of Surgery Summer Studentship, Dr. Rex Boake Studentship in Urology © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e23-e23 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jordan Bekkema More articles by this author Keith Rourke More articles by this author Expand All Advertisement Loading ...

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)0.003
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 score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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".

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Published2021
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