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Record W2604159387 · doi:10.1016/j.juro.2017.02.1353

PD29-01 COMPLICATIONS AND INTERVENTIONS IN PATIENTS WITH ARTIFICIAL URINARY SPHINCTERS.

2017· article· en· W2604159387 on OpenAlexaboutno aff
Vladimir Ruzhynsky, Christopher J.D. Wallis, Sidney B. Radomski, Refik Saskin, Lesley K. Carr, Robert K. Nam, Armando J. Lorenzo, Sender Herschorn

Bibliographic record

VenueThe Journal of Urology · 2017
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionPopulationArtificial urinary sphincterUrinary incontinenceGeneral surgeryRetrospective cohort studyUrethral sphincterAnal sphincterSurgeryGynecologyNursing

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyTrauma/Reconstruction/Diversion: Urethral Reconstruction (including Stricture, Diverticulum) II1 Apr 2017PD29-01 COMPLICATIONS AND INTERVENTIONS IN PATIENTS WITH ARTIFICIAL URINARY SPHINCTERS. Vladimir A Ruzhynsky, Christopher JD Wallis, Sidney B Radomski, Refik Saskin, Lesley Carr, Robert K Nam, Armando Lorenzo, and Sender Herschorn Vladimir A RuzhynskyVladimir A Ruzhynsky More articles by this author , Christopher JD WallisChristopher JD Wallis More articles by this author , Sidney B RadomskiSidney B Radomski More articles by this author , Refik SaskinRefik Saskin More articles by this author , Lesley CarrLesley Carr More articles by this author , Robert K NamRobert K Nam More articles by this author , Armando LorenzoArmando Lorenzo More articles by this author , and Sender HerschornSender Herschorn More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.1353AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The artificial urinary sphincter (AUS) is the most widely known treatment for male stress urinary incontinence. However, there are a lack of population-based data regarding rates of long-term AUS-related complications, including the need for revision/removal and reimplantation. We sought to characterize long-term rates of AUS revision/removal and reimplantation among all patients undergoing initial AUS insertion in the province of Ontario. Further, we sought to identify risk factors for these outcomes. METHODS We conducted a population-based, retrospective cohort study of all male patients who underwent AUS implantation from 1994-2013 in Ontario, Canada, a single payer government-funded health system. Hospital procedure codes and physician billing codes were used to identify patients who had initial AUS treatment and a subsequent revision/removal, or reimplantation. The Kaplan-Meier method and multivariable Cox proportional hazards models were used to examine the cumulative incidence of AUS reimplantation and revision/removal and to identify risk factors, respectively. RESULTS A total of 1632 male patients underwent implantation of AUS between 1994 and 2013. Overall, 10-year AUS reimplantation and revision/removal-free survival rates were 73.3% and 65.7%, respectively. Pre-implantation radiotherapy was not significantly associated with the risk of AUS reimplantation (p=0.17) or revision/removal (p=0.95). The risk of AUS reimplantation was significantly lower for patients who underwent AUS insertion at a hospital in the highest volume quartile of AUS surgeries (what is the quartile/yr) (Hazard Ratio (HR)=0.55, 95% CI 0.37-0.82), compared to those in the lowest quartile. Increasing comorbidity was associated with an increasing risk of AUS removal/revision (p=0.0008). Patient age at the time of implantation, region of residence, income quintile, and hospital type (academic vs. community) were not significantly associated with AUS reimplantation or revision/removal. CONCLUSIONS Most men who undergo AUS placement will still have a device in situ, without repeat surgeries, at 10 years following insertion. Radiotherapy does not appear to increase the risk of repeat surgeries. High volume centres have the lowest rates of reimplantation and patients with increasing morbidity have the highest risk of removal /revision. Standard clinical and epidemiologic data do not appear to predict the risk of these outcomes. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e572 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Vladimir A Ruzhynsky More articles by this author Christopher JD Wallis More articles by this author Sidney B Radomski More articles by this author Refik Saskin More articles by this author Lesley Carr More articles by this author Robert K Nam More articles by this author Armando Lorenzo More articles by this author Sender Herschorn More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0800.016

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 head0.045
GPT teacher head0.309
Teacher spread0.265 · 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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Citations0
Published2017
Admission routes1
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