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Record W4293255274 · doi:10.48083/mbxr6354

Pelvic Fracture Urethral Injury in Females

2022· article· en· W4293255274 on OpenAlexvenueno aff
Pankaj Joshi, Marco Bandini, Christian Yepes, Shreyas Bhadranavar, Vipin Sharma, Sandeep Bafna, Sanjay Kulkarni

Bibliographic record

VenueSociété Internationale d’Urologie Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePelvic fractureUrethraUrethroplastySurgeryFistulaUrinary incontinenceAnastomosisPelvis

Abstract

fetched live from OpenAlex

Background Pelvic fracture urethral injuries (PFUI) in females are very rare. The available literature on the management of this condition is scarce and not clear, mainly because of limited experience among reconstructive surgeons. We present our experience of management of these complex urethral injuries in female patients. Materials and Methods We collected data, retrospectively and prospectively for 22 female patients referred to our center for PFUI repair between 1995 and 2021. During the clinical assessment of these complex injuries, following our internal institutional protocol, all patients underwent pelvic MRI (bladder and urethra are filled with saline solution and jelly to enhance the urethral lumen and the level of the distraction) before anastomotic urethroplasty. Results PFUI compromised the mid urethra in 10 patients (45.5%). A transabdominal approach was used in 8 patients (80%), and urethra-vaginal fistula repair was undertaken in 6 patients (60%). After a median follow-up of 36 months, only 1 patient with proximal PFUI required a surgical revision without compromising urinary continence. Conclusions The most common site of urethral involvement in pelvic fracture is mid urethral, which is owing to avulsion. Urethra-vaginal fistula should be suspected. Treatment consists in anastomotic urethroplasty, mainly through the abdominal approach.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.046
GPT teacher head0.351
Teacher spread0.305 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

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