Estimating the long-term impact of a prophylactic human papillomavirus 16/18 vaccine on the burden of cervical cancer in the UK
Bibliographic record
Abstract
To predict the public health impact on cervical disease by introducing human papillomavirus (HPV) vaccination in the United Kingdom, we developed a mathematical model that can be used to reflect the impact of vaccination in different countries with existing screening programmes. Its use is discussed in the context of the United Kingdom. The model was calibrated with published data. The impact of vaccination on cervical cancer and deaths, precancerous lesions and screening outcomes were estimated for a vaccinated cohort of 12-year-old girls, among which it is estimated that there would be a reduction of 66% in the prevalence of high-grade precancerous lesions and a 76% reduction in cervical cancer deaths. Estimates for various other measures of the population effects of vaccination are also presented. We concluded that it is feasible to forecast the potential effects of HPV vaccination in the context of an existing national screening programme. Results suggest a sizable reduction in the incidence of cervical cancer and related deaths. Areas for future research include investigation of the beneficial effects of HPV vaccination on infection transmission and epidemic dynamics, as well as HPV-related neoplasms in other sites.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".