Peri‐Urethral and Anterior Vaginal Wall Masses: Does Radiological Imaging Change the Predicted Diagnosis?
Bibliographic record
Abstract
INTRODUCTION: This study aims to determine the accuracy of radiological imaging compared with surgical pathology in patients with periurethral (PU) and anterior vaginal wall (AVW) lesions. METHODS: This study is a retrospective analysis of 126 women who underwent surgical treatment for PU and AVW masses between 2011 and 2020. Clinicopathological data were extracted along with radiological findings from medical records. The primary outcome was the diagnostic accuracy of preoperative imaging compared to the gold standard, pathological diagnosis. The secondary outcome was the rate of imaging correcting the clinical diagnosis. RESULTS: A total of 126 women with a median age of 42 underwent surgical treatment for PU and AVW masses. The most diagnoses were periurethral cysts (PUC) (52%) and urethral diverticulum (UD) (39%). Clinical diagnosis was accurate in 102 cases (81%) for the group of pathological diagnoses. Magnetic resonance imaging (MRI) and transvaginal ultrasound (TV US) were performed in 82 (65%) and 22 (17%) cases. The accuracy of MRI and TV US for the diagnosis of PU and AVW lesions was 76% and 82%, respectively. MRI and TV US corrected the clinical diagnosis in five (6%) and two (9%) cases, respectively. Voiding cystourethrography (VCUG) and double balloon urethrography (DBU), each performed in six (5%) cases, were accurate in four (67%) and three (50%) cases. No statistical difference was found for any imaging modality compared to clinical diagnosis. CONCLUSION: Clinical diagnosis based on pelvic and cystoscopy examinations was sufficient for diagnosing PU and AVW masses and was not significantly different from imaging diagnosis. Imaging may be helpful with preoperative surgical planning in selected cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".