Population-level impact of switching to 1-dose human papillomavirus vaccination in high-income countries: examining uncertainties using mathematical modeling
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Bibliographic record
Abstract
BACKGROUND: A concern in high-income countries is that switching to 1-dose human papillomavirus (HPV) vaccination could cause a rebound in HPV infection and cervical cancer if 1-dose efficacy or duration were inferior to 2 doses. Using mathematical modeling and up-to-date trial-based data, we projected the population-level effectiveness of switching from 2-dose to 1-dose vaccination under different vaccine efficacy and duration assumptions in high-income countries. METHODS: We used HPV-ADVISE (Agent-based Dynamic model for VaccInation and Screening Evaluation), a transmission-dynamic model of HPV infection and cervical cancer, varying key model assumptions to identify those with the greatest impact on projections of HPV-16 and cervical cancer incidence over time: 1) 1-dose vaccine efficacy and vaccine duration, 2) mechanisms of vaccine efficacy and duration over time, 3) midadult (>30 years of age) sexual behavior, 4) progression to cervical cancer among midadults, and 5) vaccination coverage and programs. RESULTS: In high-income countries, 1-dose vaccination would cause no appreciable rebound in HPV-16 infection, except for a limited rebound under the most pessimistic assumptions of vaccine duration (average, 25 years), because 1) the switch would occur when HPV prevalence is low because of high 2-dose vaccination coverage and 2) individuals would be protected during their peak ages of sexual activity (<35 to 40 years of age). Our model projects a more limited rebound in cervical cancer because of a shift to older age at infection, resulting in fewer life-years left to potentially develop cancer. Projections were robust when varying key model assumptions. CONCLUSIONS: High protection during peak ages of sexual activity in high-income countries would likely mitigate any potential rebounds in HPV infection and cervical cancer under the most pessimistic assumptions of 1-dose efficacy and duration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 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 it