Human papillomavirus prevalence in unvaccinated heterosexual males following a national female vaccination program
Bibliographic record
Abstract
Background: In Australia, high uptake of the quadrivalent human papillomavirus (4vHPV) vaccine has led to reductions in the prevalence of human papillomavirus (HPV) genotypes 6, 11, 16, and 18 in women and girls aged ≤25 years. We evaluated the impact of the program impact on HPV prevalence in unvaccinated male subjects. Methods: Sexually active heterosexual male subjects aged 16-35 years were recruited in 2014-2016. Participants provided a self-collected penile swab sample for HPV genotyping (Roche Linear Array) and completed a demographic and risk factor questionnaire. Results: The prevalence of 4vHPV genotypes among 511 unvaccinated male subjects was significantly lower in those aged ≤25 than in those aged >25 years: 3.1% (95% confidence interval, 1.5%-5.7%) versus 13.7% (8.9%-20.1%), respectively (P < .001); adjusted prevalence ratio, 0.22 (.09-.51; P < .001). By contrast, the prevalence of high-risk HPV genotypes other than 16 and 18 remained the same across age groups: 16.8% (95% confidence interval, 12.6%-21.9%) in men aged ≤25 years and 17.9% (12.4%-25.0%) in those aged >25 years (P = .76); adjusted prevalence ratio, 0.98, (.57-1.37; P = .58). Conclusions: A 78% lower prevalence of 4vHPV genotypes was observed among younger male subjects. These data suggest that unvaccinated men may have benefited from herd protection as much as women from a female-only HPV vaccination program with high 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".