Recurrence of High-Grade Vulvar Intraepithelial Neoplasia After Treatment With Excision Compared With Imiquimod
Bibliographic record
Abstract
OBJECTIVE: To evaluate long-term recurrence rates and time to first recurrence for human papillomavirus (HPV)-associated high-grade vulvar intraepithelial neoplasia (VIN) by initial treatment. METHODS: This was a retrospective cohort study of patients treated with excision, imiquimod, or laser for HPV-associated VIN grade 2-3 at a high-risk colposcopy center. We collected demographic, clinical, and longitudinal pathology data. Given the small number (n=15), the cohort of patients treated with laser were excluded from analyses. We performed χ 2 and Wilcoxon rank-sum tests to compare the rates of recurrence and median time to first recurrence by treatment modality. Univariate and multivariate analyses were conducted to compare predictors of recurrence and time to recurrence. Multivariate models were adjusted for side effects or barriers to imiquimod use, lesion focality, and initial histology based on significant findings in the univariate models. RESULTS: Three hundred fifteen patients met the criteria for inclusion, 231 treated with excision and 84 with imiquimod. Median follow-up time from initial diagnosis was 36 months. Recurrence rates and median time to recurrence with imiquimod (40.5% and 7.4 months) and excision (34.6% and 11.3 months, P =.34, P =.38) did not differ significantly. In univariate analysis, positive margins (odds ratio [OR] 4.68, 95% CI, 2.53-8.62), multifocal disease (OR 2.27, 95% CI, 1.19-4.33), and presence of carcinoma in situ on initial diagnosis (OR 6.21, 95% CI, 1.45-26.6) were predictors of recurrence after excision. Only the presence of side effects or barriers to imiquimod use (OR 2.46, 95% CI, 1.01-6.02) was significant in the univariate model for recurrence after imiquimod. No significant difference remained for the odds of recurrence after treatment with imiquimod compared with excision in the multivariate model (OR 1.28, 95% CI, 0.77-2.14); there was similarly no significant difference in the multivariate model of time to recurrence (hazard ratio 1.41, 95% CI, 0.86-2.30). CONCLUSION: In appropriately selected patients, imiquimod appears to have outcomes similar to those of excision for the prevention of recurrent HPV-associated VIN.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".