Male Circumcision and Genital Human Papillomavirus (HPV) Infection in Males and Their Female Sexual Partners: Findings From the HPV Infection and Transmission Among Couples Through Heterosexual Activity (HITCH) Cohort Study
Bibliographic record
Abstract
BACKGROUND: Previous studies examining the association between male circumcision (MC) and human papillomavirus (HPV) infections have reported inconsistent results. We used data from the HPV Infection and Transmission Among Couples Through Heterosexual Activity (HITCH) cohort study to examine the association between MC and HPV infections in males and their female sexual partners. METHODS: We enrolled monogamous couples in a longitudinal study between 2005 and 2011 in Montreal, Canada. We used logistic and Poisson regression models with propensity score adjustment to estimate odds ratios (ORs) and rate ratios for the association between MC and the prevalence, transmission, and clearance of HPV infections. RESULTS: Four hundred thirteen couples were included in our study. The prevalence OR for the association between MC and baseline infections was 0.81 (95% confidence interval [CI], .56-1.16) in males and 1.05 (95% CI, .75-1.46) in females. The incidence rate ratio for infection transmission was 0.59 (95% CI, .16-2.20) for male-to-female transmission and 0.77 (95% CI, .37-1.60) for female-to-male transmission. The clearance rate ratio for clearance of infections was 0.81 (95% CI, .52-1.24). CONCLUSIONS: We found little evidence of an association between MC and HPV infection prevalence, transmission, or clearance in males and females. Further longitudinal couple-based studies are required to investigate this association.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".