Incidence and Clearance of Anal Human Papillomavirus Infection in 16 164 Individuals, According to Human Immunodeficiency Virus Status, Sex, and Male Sexuality: An International Pooled Analysis of 34 Longitudinal Studies
Bibliographic record
Abstract
BACKGROUND: Understanding the natural history of anal high-risk human papillomavirus (hrHPV) infection is key for designing anal cancer prevention programs but has not been systematically characterized. METHODS: We reanalyzed data from 34 studies including 16 164 individuals in 6 risk groups defined by human immunodeficiency virus (HIV) status, sex, and male sexuality: men who have sex with men (MSM) and people with HIV (MSMWH), HIV-negative MSM, women with HIV (WWH), HIV-negative women, men who have sex with women (MSW) with HIV (MSWWH), and HIV-negative MSW. We used Markov models to estimate incidence and clearance of 13 hrHPV types and their determinants. RESULTS: Human papillomavirus (HPV) 16 had the highest incidence-clearance ratio of the hrHPV types. MSMWH had the highest hrHPV incidence (eg, 15.5% newly HPV-16 infected within 2 years), followed by HIV-negative MSM (7.5%), WWH (6.6%), HIV-negative women (2.9%), MSWWH (1.7%), and HIV-negative MSW (0.7%). Determinants of HPV-16 incidence included HIV status and number of sexual partners for MSM, women, and MSW, and anal sex behavior for MSM only. HPV-16 clearance was lower for people with HIV (PWH) and lower for prevalent than incident infection. Among MSM, increasing age was associated with lower clearance of prevalent, but not incident, HPV-16 infection. CONCLUSIONS: This robust and unifying analysis of anal hrHPV natural history is essential to designing and predicting the impact of HPV vaccination and HPV-based screening programs on anal cancer prevention, particularly in MSM and PWH. Importantly, it demonstrates the higher carcinogenic potential of longstanding anal prevalent hrHPV infection than more recent incident infection.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".