Circumcision and Acquisition of Human Papillomavirus Infection in Young Men
Bibliographic record
Abstract
BACKGROUND: The role of circumcision in male HPV acquisition is not clear. METHODS: Male university students (aged 18-20 years) were recruited from 2003 to 2009 and followed up triannually. Shaft/scrotum, glans, and urine samples were tested for 37 α human papillomavirus (HPV) genotypes. Cox proportional hazards methods were used to evaluate the association between circumcision and HPV acquisition. Logistic regression was used to assess whether the number of genital sites infected at incident HPV detection or site of incident detection varied by circumcision status. RESULTS: In 477 men, rates of acquiring clinically relevant HPV types (high-risk types plus types 6 and 11) did not differ significantly by circumcision status (hazard ratio for uncircumcised relative to circumcised subjects: 0.9 [95% confidence interval{CI}: 0.7-1.2]). However, compared with circumcised men, uncircumcised men were 10.1 (95% CI: 2.9-35.6) times more likely to have the same HPV type detected in all 3 genital specimens than in a single genital specimen and were 2.7 (95% CI: 1.6-4.5) times more likely to have an HPV-positive urine or glans specimen at first detection. CONCLUSIONS: Although the likelihood of HPV acquisition did not differ by circumcision status, uncircumcised men were more likely than circumcised men to have infections detected at multiple genital sites, which may have implications for HPV transmission.
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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 | 0.003 |
| 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".