Risk of HIV acquisition among circumcised and uncircumcised young men with penile human papillomavirus infection
Bibliographic record
Abstract
OBJECTIVES: There are very few data from men on the risk of HIV acquisition associated with penile human papillomavirus (HPV) infection and no data on the potential modifying effect of male circumcision. Therefore, this study evaluated whether HPV is independently associated with risk of HIV. DESIGN: A cohort study of HPV natural history nested within a randomized control trial of male circumcision to reduce HIV incidence in Kisumu, Kenya. METHODS: Prospective data from 2519 men were analyzed using 6-month discrete-time Cox models to determine if HIV acquisition was higher among circumcised or uncircumcised men with HPV compared to HPV-uninfected men. RESULTS: Risk of HIV acquisition was nonsignificantly increased among men with any HPV [adjusted hazard ratio (aHR) 1.72; 95% confidence interval (CI) 0.94-3.15] and high-risk HPV (aHR 1.92; 95% CI 0.96-3.87) compared to HPV-uninfected men, and estimates did not differ by circumcision status. Risk of HIV increased 27% with each additional HPV genotype infection (aHR 1.27; 95% CI 1.09-1.48). Men with persistent (aHR 3.27; 95% CI 1.59-6.72) or recently cleared (aHR 3.05; 95% CI 1.34-6.97) HPV had a higher risk of HIV acquisition than HPV-uninfected men. CONCLUSIONS: Consistent with the findings in women, HPV infection, clearance, and persistence were associated with an increased risk of HIV acquisition in men. Given the high prevalence of HPV in populations at risk of HIV, consideration of HPV in future HIV-prevention studies and investigation into mechanisms through which HPV might facilitate HIV acquisition are needed.
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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 | 0.000 | 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) | 0.002 | 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".