Bibliographic record
Abstract
BACKGROUND: Female urethral stricture (FUS) is a rare entity that causes great morbidity and suffering in those affected. As the available scientific data is sparce, there are no formal guidelines or standard of care for this disease. METHODS: This is a narrative review of the surgical management for female urethral stricture. The literature review was performed on PubMed. Articles were limited to English, but there was no limitation in terms of date. RESULTS: Management of FUS is divided between endoscopic and open surgical repair. Urethral dilation with or without urethrectomy can be offered as a first-line treatment. However, the rate of success of this procedure remains inferior to open surgical repair, and its efficacy decreases with the number of previous dilations. For distal urethral strictures, distal urethrectomy and advancement meatoplasty may be considered. Vaginal flaps are readily available, easy to harvest, well-vascularized, and allow for a dorsal or ventral orientation urethroplasty. The results of this procedure are promising, but most studies are small and retrospective. Labia flaps are easily accessible, wet, hairless, and elastic. The main limitations with the use of vaginal or labial tissues are co-existing conditions such as lichen sclerosis or vaginal atrophy, which may affect future results. Vaginal and labial graft urethroplasty can be used when it is not possible to mobilize an adequate flap. Stricture-free rates of this technique are variable. In cases of more severe stricture, an augmentation urethroplasty using buccal mucosa graft may be necessary. The techniques used in FUS replicate those for male urethral strictures, where both ventral and dorsal approaches can be utilized. CONCLUSIONS: Although there is growing interest in the field, the optimal management of FUS remains to be determined.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".