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Record W1975158043 · doi:10.5489/cuaj.1407

Outcome of urethral strictures treated by endoscopic urethrotomy and urethroplasty

2014· article· en· W1975158043 on OpenAlexvenueno aff
Javier Tinaut-Ranera, Miguel Ángel Arrabal‐Polo, Sergio Merino‐Salas, Mercedes Nogueras-Ocaña, Víctor López‐León, Francisco Palao-Yago, Miguel Arrabal-Martı́n, C. Lahoz-García, Miguel Alaminos, Armando Zuluaga‐Gómez

Bibliographic record

VenueCanadian Urological Association Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsUrethroplastyUrethrotomyMedicineUrethral strictureSurgeryRetrospective cohort studyUrethra

Abstract

fetched live from OpenAlex

INTRODUCTION: We analyze the outcomes of patients with urethral stricture who underwent surgical treatment within the past 5 years. METHODS: This is a retrospective study of male patients who underwent surgery for urethral stricture at our service from January 2008 to June 2012. We analyzed the comorbidities, type, length and location of the stricture and the surgical treatment outcome after endoscopic urethrotomy, urethroplasty or both. RESULTS: In total, 45 patients with a mean age of 53.7 ± 16.7 years underwent surgical treatment for urethral stricture. Six months after surgery, 46.7% of the patients had a maximum urinary flow greater than 15 mL/s, whereas 87.3% of the patients exhibited no stricture by urethrography after the treatment. The success rate in the patients undergoing urethrotomy was 47.8% versus 86.4% in those undergoing urethroplasty (p = 0.01). Twenty percent of the patients in whom the initial urethrotomy had failed subsequently underwent urethroplasty, thereby increasing the treatment success. CONCLUSION: In most cases, the treatment of choice for urethral stricture should be urethroplasty. Previous treatment with urethrotomy does not appear to produce adverse effects that affect the outcome of a urethroplasty if urethrotomy failed, so urethrotomy may be indicated in patients with short strictures or in patients at high surgical risk.

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 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)0.000
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.237
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 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".

Quick stats

Citations22
Published2014
Admission routes1
Has abstractyes

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