Influence of Partner's Infection Status on Prevalent Human Papillomavirus Among Persons With a New Sex Partner
Bibliographic record
Abstract
BACKGROUND: We evaluated the influence of the partner's human papillomavirus (HPV) status and sexual practices on prevalent HPV infection among new couples to study HPV transmission. METHODS: Women attending university or college in Montreal, Canada, and their male partners (N = 263 couples) were enrolled in 2005-2008. HPV typing was done in self-collected vaginal swabs and clinician-collected penis and scrotum swabs. The outcome measures were overall and type-specific HPV prevalence. RESULTS: HPV was detected in 56% of women and men. Prevalence was higher among persons with infected partners (85%) than in those whose partners were negative (19%). Type-specific detection was substantially higher among women (OR = 55.2, 95% CI: 38.0-80.1) and men (OR = 58.7, 95% CI: 39.8-86.3) if their partner harbored the type under consideration. Prevalence among women and men with 10 or more lifetime partners was 15.4 (95% CI: 5.9-40.2) and 9.5 (95% CI: 4.4-19.8) times higher than among those with 1 partner. Frequent condom use was protective in men, particularly if his partner was HPV-infected (OR = 0.64, 95% CI: 0.50-0.82). This effect was attenuated among women with an infected partner (OR = 0.88, 95% CI: 0.69-1.11). CONCLUSIONS: The current partner's status was the most important risk factor for prevalent HPV infection. Condoms exerted a stronger protective effect among men than among women.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".