Different population-level vaccination effectiveness for HPV types 16, 18, 6 and 11
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
BACKGROUND: Given that the human papillomavirus (HPV) vaccine types have different durations of infectiousness and infectivity, the population-level vaccine effectiveness of these types may differ even if vaccine efficacy is identical. OBJECTIVE: To compare the type-specific effectiveness of vaccination against HPV types 16, 18, 6 and 11. METHODS: An individual-based stochastic model of HPV transmission (18 HPV-types) in a population stratified by age, gender and sexual activity was developed. Multiple parameter sets were identified by fitting the model to sexual behaviour data and age- and type-specific HPV prevalence. RESULTS: Under base case assumptions (70% coverage, 99% vaccine efficacy per act and 20 years' duration of protection), vaccinating 12-year-old girls is predicted to reduce HPV-16, HPV-18 and HPV-6/11 prevalence by 61% (80% uncertainty interval (UI) 53-77), 92% (80% UI 65-100) and 100% (80% UI 97-100), respectively, 50 years after the start of the vaccination programme. Differences in type-specific vaccine effectiveness increased over time, and decreased with improved vaccine efficacy characteristics. CONCLUSIONS: For the same vaccine efficacy, the population-level impact of HPV vaccination will most likely be different, with HPV-16, the most oncogenic type, having the lowest effectiveness. These results should be taken into account when designing and interpreting post-vaccination surveillance studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it