Vaccine-Relevant Human Papillomavirus (HPV) Infections and Future Acquisition of High-Risk HPV Types in Men
Bibliographic record
Abstract
BACKGROUND: Little is known about type-specific associations between prevalent human papillomavirus (HPV) infections and risk of acquiring other HPV types in men. Data on natural clustering of HPV types are needed as a prevaccine distribution to which postvaccine data can be compared. METHODS: Using data from a randomized controlled trial of male circumcision in Kisumu, Kenya, adjusted mean survival ratios were estimated for acquisition of any-HPV, high-risk (HR) HPV, and individual HR-HPV types among men uninfected as compared to those infected with vaccine-relevant HPV types 16, 18, 31, 45, 6, or 11 at baseline. RESULTS: Among 1097 human immunodeficiency virus-negative, uncircumcised men, 2303 incident HPV infections were detected over 2534 person-years of follow-up. Although acquisition of individual HR-HPV types varied by baseline HPV type, there was no clear evidence of shorter times to acquisition among men without vaccine-relevant HPV-16, -18, -31, -45, -6, or -11 infections at baseline, as compared to men who did have these infections at baseline. CONCLUSIONS: These prospective data on combinations of HPV infections over time do not suggest the potential for postvaccination HPV type replacement. Future surveillance studies are needed to definitely determine whether elimination of HPV types by vaccination will alter the HPV type distribution in the population.
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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 | 0.005 |
| 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 | 0.001 |
| 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".