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PD34-09 VESICO-VAGINAL FISTULA PREVALENCE AND REPAIR PATTERNS: A LARGE RETROSPECTIVE POPULATION-BASED COHORT ANALYSIS

2022· article· en· W4225257953 on OpenAlexaboutno aff
Sarah Neu, Jennifer A. Locke, Bo Zhang, Refik Saskin, Sender Herschorn

Bibliographic record

VenueThe Journal of Urology · 2022
Typearticle
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRetrospective cohort studyVesicovaginal fistulaHazard ratioFistulaPopulationCohortProportional hazards modelSurgeryCohort studyInternal medicineEnvironmental healthConfidence interval

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyCME1 May 2022PD34-09 VESICO-VAGINAL FISTULA PREVALENCE AND REPAIR PATTERNS: A LARGE RETROSPECTIVE POPULATION-BASED COHORT ANALYSIS Sarah Neu, Jennifer Locke, Bo Zhang, Refik Saskin, and Sender Herschorn Sarah NeuSarah Neu More articles by this author , Jennifer LockeJennifer Locke More articles by this author , Bo ZhangBo Zhang More articles by this author , Refik SaskinRefik Saskin More articles by this author , and Sender HerschornSender Herschorn More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002585.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: In North America vesicovaginal fistula (VVF) are most commonly due to iatrogenic injury and have a significant negative impact on quality of life. Failed surgical repair of VVF can lead to ongoing morbidity. The objective of our study is to determine the change in rate of VVF repair and failures over time, and to determine risk factors for surgical repair failure. METHODS: We completed a population-based, retrospective cohort study including all women in Ontario, Canada, aged 18 and older between 2005-2018. Patients who underwent VVF repair were identified using linked administrative databases and compared to those who required a second VVF repair for primary repair failure. Broken line regression was used to determine changes in the rate of VVF repair over time. Multivariable cox proportional hazard analysis was used to identify risk factors for VVF repair failure. RESULTS: 814 patients were identified as having undergone VVF repair. Of these patients, 117 required a second surgical repair (14%). Mean age at time of surgery was 52 years (SD 15). Most patients had undergone prior gynecological surgery (68%), and 76% were due to iatrogenic injury. Most repairs were performed by urologists (60%) and completed trans-vaginal (66%). Annual rate of VVF repair significantly decreased by 0.14/100,000 women in each year from 2005-2009, and insignificantly decreased from 2010-2018. No significant change in VVF re-repair rates were found. Predictors of VVF re-repair included iatrogenic injury as etiology of VVF (HR 2.1, 95% CI 1.3-3.9, p=0.009), and having the primary repair done with cystoscopic fulguration (HR 6.1 95% CI 3.1-11.1, p<0.0005,); protective factor was surgeon number of years in practice (21+ years - HR 0.5, 95% CI 0.3-0.9, p=0.02). Surgeons with more than 21 years of experience halve half as many patients requiring second VVF repair. CONCLUSIONS: VVF repair rates have decreased over time, however re-repair rates have remained constant over a 13-year time-period. Iatrogenic injury as the cause of VVF is twice as likely to result in the need for a re-repair, compared to other causes, and repair done with cystoscopic fulguration were 6 times as likely to fail compared to a trans-vaginal or abdominal approach. Surgeon years in practice may protect against the need for a second VVF surgery. Source of Funding: University of Toronto Functional Urology Program © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e563 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sarah Neu More articles by this author Jennifer Locke More articles by this author Bo Zhang More articles by this author Refik Saskin More articles by this author Sender Herschorn More articles by this author Expand All Advertisement PDF downloadLoading ...

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.008
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.267
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 teacher head, 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
Published2022
Admission routes1
Has abstractyes

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