Determinants of Prevalent Human Papillomavirus in Recently Formed Heterosexual Partnerships: A Dyadic-Level Analysis
Bibliographic record
Abstract
BACKGROUND: We studied features that predict the presence of human papillomavirus (HPV) in a new sexual partnership. METHODS: We analyzed data from the "HPV Infection and Transmission Among Couples Through Heterosexual Activity" (HITCH) Cohort Study of recently formed partnerships ("dyads"). Women aged 18-24 and their male partners were recruited during 2005-2010 in Montreal, Canada. We tested genital swabs for detection of 36 HPV types. We defined HPV in a partnership as the presence of 1 or more HPV types in either or both partners. Using baseline data from 482 dyads, we calculated prevalence ratios to evaluate candidate risk factors. RESULTS: Most women (88%) were unvaccinated. Sixty-seven percent of dyads harbored HPV. For 49% of dyads, both partners were HPV+. HPV was least prevalent in dyads who were in their first vaginal sex relationship (17%) and was virtually ubiquitous in dyads for which both partners had concurrent partners (96%). Dyads that always used condoms with previous partner(s) were 27% (95% confidence interval, 9%-42%) less likely to have HPV. CONCLUSIONS: The finding that condom use limited onward spread to future partners is in support of condom promotion to prevent sexually transmitted infections. Ongoing monitoring of HPV in sexual networks is needed, particularly in populations with suboptimal vaccine coverage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".