Bivalent Vaccine Effectiveness Against Type-Specific HPV Positivity: Evidence for Cross-Protection Against Oncogenic Types Among Dutch STI Clinic Visitors
Bibliographic record
Abstract
Background: Observational postmarketing studies are important to assess vaccine effectiveness (VE). We estimated VE from the bivalent human papillomavirus (HPV) vaccine against HPV positivity of vaccine and nonvaccine types in a high-risk population. Methods: We included all vaccine-eligible women from the PASSYON study, a biennial cross-sectional survey in Dutch sexually transmitted infection clinics. Vaginal swabs were analyzed using a polymerase chain reaction-based assay (SPF10-LiPA25) able to detect the 12 high-risk HPV (hrHPV) types 16/18/31/33/35/39/45/51/52/56/58/59. We compared hrHPV positivity between self-reported vaccinated (≥1 dose) and unvaccinated women, and estimated VE by a logistic mixed model. Results: We included 1087 women of which 53% were hrHPV positive and 60% reported to be vaccinated. The adjusted pooled VE against HPV-16/18 was 89.9% (81.7%-94.4%). Moreover, we calculated significant VE against nonvaccine types HPV-45 (91%), HPV-35 (57%), HPV-31 (50%), and HPV-52 (37%). Among women who were offered vaccination 5/6 years ago, we estimated similar VE against HPV-16/18 (92%) and all hrHPV types (35%) compared to women who were offered vaccination <5 years ago (83% and 33%, respectively). Conclusion: We demonstrated high VE of the bivalent vaccine against HPV-16/18 and cross-protection against HPV-45/35/31/52. Protection against HPV-16/18 was sustained up to 6 years postvaccination.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".