S12.1 Transmission of human papillomavirus infections
Bibliographic record
Abstract
Background There have been few studies of the sexual transmission of human papillomavirus (HPV) between partners. Our objective was to estimate transmission rates among persons with documented sexual exposure to an infected partner and longitudinal follow-up. Methods We analysed data from the HITCH Cohort Study, a study of recently-formed couples. Women aged 18–24 attending a university or junior college in Montreal, Canada and their male partners were eligible. Self-collected vaginal swabs and clinician-obtained swabs of epithelial cells from the penis and scrotum were tested for DNA of 36 HPV types. We analysed follow-up data at visit 2 from 179 couples who were discordant for one or more HPV types at enrolment. We defined the index partner as that which was infected with a type(s) not found in the other partner, and a transmission event as subsequent detection of that HPV type in the non-index partner. Transmission rates are expressed as the number of transmissions per 100 person-months (PM), with 95% CI estimated using Poisson regression. Results Transmission was observed in 73 partnerships. There was little difference between the male-to-female (3.5 per 100PM, 95% CI 2.7 to 4.5) and the female-to-male transmission rate (4.0 per 100PM, 95% CI 3.0 to 5.5). These rates are consistent with a per-partnership transmission probability of 0.20 (95% CI 0.16 to 0.24) over 6 months. Transmission rates did not differ with the lifetime number of partners reported by the non-index partner at enrolment or with the circumcision status of the male partner. Rates were highest when the index partner was still positive for that type at follow-up; rates of male-to-female transmission quadrupled and female-to-male transmission tripled (5.2 and 6.2 per 100PM, respectively), compared to when the index partner was negative at follow-up (1.2 and 1.8 per 100PM, respectively, p<0.05). Conclusions Transmission rates based on follow-up of discordant partners are probably underestimates of the true rate due to clearance in index partner and the depletion of susceptibles. Our results contribute to a small but growing evidence base regarding the natural history of HPV transmission and the probability of transmission. These estimates may be of utility to improve forecasting estimates from mathematical modelling efforts to project the public health impact and cost-effectiveness of HPV vaccination.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.118 | 0.027 |
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".