Estimating the number needed to vaccinate to prevent diseases and death related to human papillomavirus infection
Bibliographic record
Abstract
BACKGROUND: A vaccine against human papillomavirus (HPV) types 6, 11, 16 and 18 is now licensed for use in Canada and many other countries. We sought to estimate the number needed to vaccinate to prevent HPV-related diseases and death. METHODS: A cohort model of the natural history of HPV infection was developed. Model simulations were based on 209 different parameter sets that reproduced Canadian HPV type-specific data for infection, cervical intraepithelial neoplasia, cervical cancer and genital warts. The number needed to vaccinate was calculated as the number of women who would need to be vaccinated to prevent an HPV-related event during their lifetime. RESULTS: Among 12-year-old girls, we estimated that the number needed to vaccinate to prevent an episode of genital warts would be 8 (80% credibility interval [CrI] 5-15) and a case of cervical cancer 324 (80% CrI 195-757). These estimates were based on the assumption that the vaccine procures lifelong protection and that its efficacy is 95%. If vaccine protection is assumed to wane at 3% per year, the predicted number needed to vaccinate would increase to 14 (80% CrI 6-18) and 9080 (80% CrI 1040-does not prevent), respectively. The latter number would be greatly reduced with the addition of a booster dose, to 480 (80% CrI 254-1572). INTERPRETATION: Our model predictions suggest that vaccination with the currently available HPV vaccine may significantly reduce the incidence of genital warts, cervical intraepithelial neoplasia and cervical cancer. However, the benefits (particularly in terms of cervical cancer reduction) are highly dependent on the duration of vaccine protection, on which evidence is currently limited.
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.
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 | 0.003 | 0.019 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".