Sex-specific patterns of incidence, transmission, and concordance of human papillomavirus infections in newly formed heterosexual couples.
Bibliographic record
Abstract
Background: Understanding human papillomavirus (HPV) transmission dynamics within couples is necessary for optimal vaccine strategies.We used data from the Transmission Reduction and Prevention with HPV Vaccination (TRAP-HPV) study to estimate sex-specific incidence and transmission rates. Methods:The TRAP-HPV study enrolled (2014-2022) new (≤6 months) heterosexual couples aged 18+ in Montreal, Canada.Participants (n=308) were randomized into four comparison groups (both partners vaccinated against HPV or hepatitis A, or each received one or the other vaccine).Genital samples, collected at 0, 2, 4, 6, 9, and 12 months, were genotyped for 36 HPV types.We performed time-to-event analyses for vaccine-targeted HPVs (6/11/16/18/31/33/45/52/58) and HPVs phylogenetically related (35/39/44/59/67/68/70) and unrelated (26/34/40/42/51/53/54/56/61/62/66/69/71/72/73/81/82/83/84/89) to vaccine-targeted types, using type-specific HPV infections as the unit of analysis.Results: Vaccination was weakly associated with lower incidence of vaccine-targeted HPV for males; incidence rates (in events/1000 months) were 0.99 (95%CI: 0.17, 3.07) and 1.67 (95%CI: 0.75, 3.51) in the two groups with vaccinated males versus 2.42 (95%CI: 0.97, 7.63) and 3.35 (95%CI: 1.95, 6.30) in the two groups with unvaccinated males.There was no consistent pattern of protection against incident HPV detection in females and no indication that recent vaccination was associated with lower transmission in discordant couples or with protection for one's partner.Results were similar for the three HPV groups. Conclusions:In this population of sexually active adults, we did not find conclusive evidence that recent vaccination was associated with protection for oneself or one's partner.Findings should not be generalized to younger populations.
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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 | 0.001 |
| 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".