Cervical Infection With Vaccine-Associated Human Papillomavirus (HPV) Genotypes as a Predictor of Acquisition and Clearance of Other HPV Infections
Bibliographic record
Abstract
BACKGROUND: Recent birth cohorts vaccinated against human papillomavirus (HPV) may be protected against up to 4 genotypes (HPV-6, -11, -16, and -18). If natural competition exists between these and other HPV types, then the prevalence of other types may increase after vaccination. METHODS: Cohort information from 3 studies was used to compare acquisition and clearance of 30 different HPV types (individually and grouped by species), according to infection status with vaccine-targeted types at baseline and the time of the index infection, respectively. Hazard ratios (HRs) were adjusted for predictors of multiple-type infection. RESULTS: Among 3200 females across all studies, 857 were infected with HPV at baseline, and 994 acquired new infections during follow-up. Females infected with HPV-16 were at higher risk of acquiring other α-9 HPV types (HR, 1.9; 95% confidence interval [CI], 1.2-3.0) but at similar risk of clearing existing α-9 HPV infections (HR, 0.9; 95% CI, .7-1.3). Females infected with vaccine-targeted types were generally at higher risk of acquiring additional types (HRs, > 1.0) and at equal risk of clearing existing infections. Accounting for multiple comparisons, none of the HRs of < 1.0 or >1.0 were statistically significant in our analyses of acquisition or clearance. CONCLUSIONS: Vaccine-targeted HPV types do not appear to compete with other types, suggesting that HPV type replacement is unlikely to occur.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".