Modeling Human Papillomavirus Vaccine Effectiveness: Quantifying the Impact of Parameter Uncertainty
Bibliographic record
Abstract
The development of models is based on assumptions, which inevitably embed a level of uncertainty. Quantifying such uncertainty is particularly important when modeling human papillomavirus (HPV) vaccine effectiveness; the natural history of infection and disease is complex, and age- and type-specific data remain scarce and incomplete. The aim of this study was to predict the impact of HPV-6/11/16/18 vaccination, using a cohort model and measuring parameter uncertainty. An extensive fitting procedure was conducted, which identified 164 posterior parameter combinations (out of 200,000 prior parameter sets) that fit simultaneously HPV type-specific incidence and prevalence data for infection, cervical intraepithelial neoplasia (CIN), and squamous cell carcinoma (SCC). Results based on these posterior parameter sets suggest that vaccinating girls aged 12 years (vaccine efficacy = 95%, no waning) would reduce their lifetime risk of HPV infection, CIN1, CIN2/3, and SCC by 21% (80% credibility interval: 17, 29), 24% (80% credibility interval: 17, 31), 49% (80% credibility interval: 36, 60), and 61% (80% credibility interval: 47, 73), respectively. If vaccine efficacy is reduced or vaccine protection is assumed to wane, uncertainty surrounding predictions widens considerably. Important priorities for future research are to understand the role of natural immunity and to measure the duration of vaccine protection because results were most sensitive to these parameters.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".