Identifying risk factors for prevalent anal human papillomavirus type 16 infection in women living with HIV
Bibliographic record
Abstract
BACKGROUND: Women living with HIV (WLHIV) have a high risk of anal cancer. Identifying risk factors for anal HPV 16 infection, the most significant risk factor for anal cancer, is essential for prevention and screening strategies. METHODS: In the EVVA Cohort study, 151 WLHIV had cervical and anal HPV testing with genotyping every 6 months for 2 years, while demographic and clinical data were collected via questionnaires and chart reviews. Here, we present results of baseline data analyzed using multivariable logistic regression. RESULTS: Among 150 women with adequate HPV test results at baseline, HPV 16 DNA was detected anally in 23 (15.3%; 95%CI:10.4-22.1) and cervically in 5 (3.3%; 95%CI:1.4-7.8). In multivariable analysis, current smoking (OR = 6.0; 95%CI: 1.5-23.9), nadir CD4 count ≤ 200 cells/μL (OR = 8.4; 95%CI: 2.0-34.3), prevalent cervical HPV 16 (OR = 14.7; 95%CI: 1.0-222.5) and anogenital herpes in previous 6 months (OR = 9.8, 95%CI: 1.7-56.8) were associated with prevalent anal HPV 16. CONCLUSIONS: Knowledge of risk factors can help identify WLHIV at greatest risk of anal HPV 16 infection and, potentially, developing subsequent anal cancer. Identification of the subgroup of these women in whom HPV 16 persists could be an early step in the algorithm of anal cancer screening.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".