Management of Vulvar Cancer Precursors: A Survey of the International Society for the Study of Vulvovaginal Disease
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to determine how experts treat vulvar high-grade squamous intraepithelial neoplasia (VHSIL) and differentiated vulvar intraepithelial neoplasia (dVIN). METHOD: A 26-question survey was designed through a literature review, reviewed by the Survey Committee of the International Society for the Study of Vulvovaginal Disease (ISSVD), and distributed to all ISSVD members via e-mail in January 2019. RESULTS: Overall, 90 of 441 physician members consented to participate and 78 of 90 were eligible to complete the survey. Most respondents were gynecologists (77%), followed by dermatologists (12%). Forty-five percent responded that their pathology was being reported using the 2015 ISSVD terminology of vulvar squamous intraepithelial lesions. The most common first-line treatments were as follows: unifocal VHSIL-excision (65%), multifocal VHSIL-imiquimod 5% (45%), VHSIL in a hair-bearing area-excision (69%), and clitoral disease-imiquimod 5% (47%). In the recurrent VHSIL, excision was favored (28%), followed by imiquimod 5% (26%) and laser (19%). Differentiated vulvar intraepithelial neoplasia was most often first treated with excision (82%), and more patients were referred to gynecologic oncology. Most patients were seen in follow-up at 3 months (range: 1 week-6 months). Sixty-seven respondents provided 26 different ways to follow treated patients, which were most commonly every 6 months for 2 years and then yearly (25%), followed by every 6 months indefinitely (18%). CONCLUSIONS: Treatment of VHSIL and dVIN varies among vulvar experts with excision being the most common treatment, except in multifocal VHSIL where imiquimod is commonly used. There is wide variation in how patients are followed after treatment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".