Human papillomavirus incidence and transmission by vaccination status among heterosexual couples
Bibliographic record
Abstract
BACKGROUND: Understanding human papillomavirus (HPV) transmission dynamics within couples is necessary for optimal vaccine catch-up 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. The study employed a 2 × 2 factorial design. Participants (n = 308) were randomized into four groups: neither partner vaccinated against HPV, only the male partner vaccinated against HPV, only the female partner vaccinated against HPV, or both partners vaccinated against HPV. 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: Participants had a mean age of 25.5 years (SD 6.0), and a median of 6 (IQR: 2-15) lifetime sexual partners. Among 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 groups with unvaccinated males. Results were similar for the three HPV groups. CONCLUSIONS: 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. Findings should not be generalized to younger populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".