Potential lives saved in 73 countries by adopting multi‐cohort vaccination of 9–14‐year‐old girls against human papillomavirus
Bibliographic record
Abstract
Up to 2016, low- and middle-income countries mostly introduced routine human papillomavirus (HPV) vaccination for just a single age-cohort of girls each year. However, high-income countries have reported large reductions in HPV prevalence following "catch-up" vaccination of multiple age-cohorts in the year of HPV vaccine introduction. We used the mathematical model PRIME to project the incremental impact of vaccinating 10- to 14-year-old girls compared to routine HPV vaccination only in the same year that routine vaccination is expected to be introduced for 9-year-old girls across 73 low- and lower-middle-income countries. Adding multiple age-cohort vaccination could increase the number of cervical cancer deaths averted by vaccine introductions in 2015-2030 by 30-40% or an additional 1.23-1.79 million over the lifetime of the vaccinated cohorts. The number of girls needed to vaccinate to prevent one death is 101 in the most pessimistic scenario, which is only slightly greater than that for routine vaccination of 9-year-old girls (87). These results hold even when assuming that girls who have sexually debuted do not benefit from vaccination. Results suggest that multiple age-cohort vaccination of 9- to 14-year-old girls could accelerate HPV vaccine impact and be cost-effective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".