Modeling Human Papillomavirus Vaccine Effectiveness: Quantifying the Impact of Parameter Uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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