Modeling the Sexual Transmissibility of Human Papillomavirus Infection using Stochastic Computer Simulation and Empirical Data from a Cohort Study of Young Women in Montreal, Canada
Bibliographic record
Abstract
The authors estimated plausible ranges of the probability of human papillomavirus (HPV) transmission per coital act among newly forming couples by using stochastic computer simulation. Comparative empirical data were obtained in 1996-2001 from a cohort study of female university students in Montreal, Canada. Female prevalence and frequency of sexual intercourse and condom use were set equal to those in the cohort. Simulations included 240 combinations of male prevalence, the relative risk for protected versus unprotected sex, and per-act transmission probabilities. Those that produced expected HPV incidence within the 95% confidence interval observed in the cohort were selected. The observed 6-month cumulative incidence following acquisition of a new partner was 17.0% (95% confidence interval: 11.4, 23.0). Expected incidences consistent with those from cohort findings occurred in 54/240 simulations. The range of per-act transmission probabilities was 5-100% (median, 40%). Male HPV prevalence was the same as or greater than that for women in all consistent simulations. Varying condom effectiveness did not produce better-fitting data. This simulation suggests that HPV transmissibility is several-fold higher than that for other viral sexually transmitted infections such as human immunodeficiency virus or herpes simplex virus 2. With high transmissibility, any potential protective effect of condoms would disappear over multiple intercourse acts, underlining the need for an effective HPV vaccine.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".