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Record W2472604947

Female neo-urethral reconstruction with a modified neurovascular pudendal thigh flap (Singapore flap): initial experience.

2007· article· en· W2472604947 on OpenAlexaff
Kevin C. Zorn, Alex Bzrezinski, Benoît St-Denis, Jacques Corcos

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineNeurovascular bundleSurgeryNeck of urinary bladderUrinary incontinenceUrethroplastyUrethraUrethral strictureUrinary continenceThighProstatectomyUrinary bladderProstate
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: Complete urethral reconstruction in women is an extreme challenge for urologists. We report a new approach to this challenging procedure, using a modified neurovascular pudendal thigh flap (Singapore flap) for neo-urethral reconstruction in two patients at our institution. MATERIALS AND METHODS: A Singapore flap was raised and transposed to the vagina in two patients with destroyed urethras following several previous uro-gynecologic procedures. In one case, the flap was tubularized distally and anastomosed proximally to the bladder neck. In the second case, the flap served to cover a urethroplasty inlay. RESULTS: We are presently reporting mainly on the surgical technique. At 24 and 27 months respectively, the two flaps are viable and appear to fulfill their designated roles. The first case developed bladder neck stricture 2 months after surgery, and urethral dilation was performed successfully. Urinary incontinence still remains an issue in one of these two patients, and further continence procedures have been used. CONCLUSIONS: It has been found that the neurovascular pudendal thigh flap, initially described for vaginal reconstruction, is an attractive technique to aid in complete female neo-urethral reconstruction. Further follow-up of patients is necessary to address the issue of continence.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.623

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 integrity0.0000.000
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 head0.037
GPT teacher head0.265
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

Citations5
Published2007
Admission routes1
Has abstractyes

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