An update on urethral diverticula: Results from a large case series
Bibliographic record
Abstract
INTRODUCTION: We aimed to describe the presentation, investigations, and management of patients with urethral diverticula and to review the importance of magnetic resonance imaging (MRI) in the diagnosis and surgical management of urethral diverticula. METHODS: This was a retrospective review of female patients who underwent urethral diverticulectomies. This study was approved by the research ethics review board. Data was collected on patient demographics, presenting symptoms, investigations performed, operative technique, and minimum of two-year followup. RESULTS: A total of 17 patients were included in this study, with a median age of 43 years. Most patients (70%) presented with a palpable vaginal lump; 64% presented with either lower urinary tract symptoms (LUTS) or recurrent urinary tract infections (UTIs). Patients underwent a preoperative MRI, which demonstrated that 59% of diverticula were distal and 53% were locally round. These imaging findings were consistent with the operative findings. MRI also demonstrated communication between the urethral diverticulum and the urethral lumen in 80% of cases, compared to only 47% endoscopically. CONCLUSIONS: The most common presentation of a woman with a urethral diverticulum is with either a palpable vaginal lump, LUTS, or recurrent UTIs. A high index of suspicion is required. Pelvic MRI appears to be an ideal imaging modality for the diagnosis of urethral diverticulum. A preoperative MRI is important to exclude alternative pathologies, appropriately counsel the patient, and assist with the surgical planning.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".