High‐risk human papillomavirus infection of the genital tract of women with a previous history or current high‐grade vulvar intraepithelial neoplasia
Bibliographic record
Abstract
Human papillomavirus (HPV) infection is associated with high-grade vulvar intraepithelial neoplasia (VIN-3). The prevalence of anogenital HPV infection in women with previously treated VIN-3 has not been documented yet. This cross-sectional study compared high-risk HPV DNA detection rates in women with past (n = 30) and current (n = 22) VIN-3 to those without current or past VIN (n = 86). HPV DNA was detected in vulvar and cervical samples with Hybrid Capture 2 (HC-2). Smoking was associated in multivariate analysis with current VIN-3 (odds ratio (OR) 8.3, 95% confidence interval (CI) 2.0-8.2) and any VIN-3 history (OR 6.5, 95% CI 2.5-16.5). High-risk HPV DNA was found on the vulva of 64%, 33%, and 20% of women with current VIN-3, past VIN-3, and without previous or current VIN, respectively. After controlling for age and smoking, high-risk HPV vulvar infection was associated with cervical high-risk HPV infection (OR 8.6, 95% CI 2.8-26.5; P = 0.001). After controlling for age, HPV infection was more often multifocal in women with current VIN-3 compared to women with previous but no current VIN-3 lesion (OR 17.6, 95% CI 1.4-227.2). Multifocal vulvar HPV infection was detected in women with previous or active VIN-3. Longitudinal studies are required to determine if the multifocality of HPV infection on the vulva could explain the high recurrence rate of VIN-3.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".