HPV Seroconversion Following Anal and Penile HPV Infection in HIV-Negative and HIV-Infected MSM
Bibliographic record
Abstract
BACKGROUND: We assessed human papillomavirus (HPV) seroconversion following anal and penile HPV infection in HIV-negative and HIV-infected men who have sex with men (MSM). METHODS: MSM aged ≥18 years were recruited in Amsterdam, the Netherlands (2010-2011), and followed up semiannually. Antibodies against 7 high-risk HPV types in baseline and 12-month serum samples were tested using a multiplex immunoassay. Baseline, 6-, and 12-month anal and penile samples were tested for HPV DNA using the SPF10-PCR DEIA/LiPA25 system. Statistical analyses were performed using logistic regression with generalized estimating equations. RESULTS: Of 644 MSM included in the analysis, 245 (38%) were HIV-infected. Median age was 38 years for HIV-negative and 47 years for HIV-infected MSM (P < 0.001). Seroconversion against ≥1 of the 7 HPV types was observed in 74 of 396 (19%) HIV-negative and 52 of 223 (23%) HIV-infected MSM at risk (P = 0.2). Incident [adjusted OR (aOR) 2.0; 95% confidence interval (CI), 1.1-3.4] and persistent (aOR 3.7; 95% CI, 1.5-9.5) anal HPV infections were independently associated with type-specific seroconversion in HIV-negative MSM. In HIV-infected MSM, there was a nonsignificant positive association between penile HPV infection at any time point and seroconversion (aOR 1.7; 95% CI, 0.9-3.2). CONCLUSIONS: Incident or persistent anal HPV infection was an independent determinant of seroconversion in HIV-negative MSM. IMPACT: Our data support that seroresponse may vary per anatomic site and that persistent HPV infections are more likely to elicit a detectable humoral immune response.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".