Multiple Human Papillomavirus Infections and Type Competition in Men
Bibliographic record
Abstract
BACKGROUND: There is little information on multiple human papillomavirus (HPV) infections and the potential for type competition in men, yet competition may impact the type-specific efficacy of HPV vaccination. METHODS: Among 2702 uncircumcised men in Kisumu, Kenya, who were seronegative for human immunodeficiency virus, the observed numbers of HPV types detected were compared with the expected number, which was simulated under the assumption of independent infections. To assess the potential for HPV type competition, adjusted odds ratios for pairwise combinations of prevalent HPV type infections were estimated using semi-Bayesian methods. RESULTS: Half of all men were HPV positive, of whom 57% had multiple HPV types. We observed men without HPV infection and with ≥4 HPV types more often than expected if infections were independent. No negative associations between individual HPV types were observed. HPV types 31, 39, 56, 58, and 59 were positively associated with both carcinogenic vaccine types HPV-16 and HPV-18 (2-sided P value <.05). CONCLUSIONS: Men who were HPV infected were likely to test positive for >1 HPV type. Cross-sectional associations between individual HPV types were positive and did not appear to be type-specific. Thus, we did not identify HPV types that are candidates for potential HPV type competition in men.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".