Time for enhancing government-led primary prevention of cervical cancer
Bibliographic record
Abstract
https://ejgo.orgCervical cancer is a preventable disease.Lei et al. [1] recently demonstrated using the Swedish nationwide registry that human papillomavirus (HPV) vaccines protected not only precancerous cervical lesions but also invasive cervical cancers.In this study, the risk of cervical cancer among women who had been vaccinated <17 years of age was 88% lower than the unvaccinated population.This effective vaccination results in a survival benefit when 'herd immunity' is achieved through high vaccination coverage in a population.One systematic review and meta-analysis by Drolet et al. [2] found that unvaccinated persons would indirectly benefit from HPV vaccination if vaccination coverage of girls and women in a population exceeds 50%.Vaccine coverage of the study of Lei et al. [1] in Sweden was 83.9% in the 2002 birth cohort and as high as 83.3%-97.7% in Australia where cervical cancer incidence continually declined over the years of vaccination [3,4].This critical role of high vaccine coverage rates in reduction of cervical cancer incidence suggest that the HPV vaccination program must be scaled to national levels.The World Health Organization (WHO) reported countries with HPV vaccines in the National Immunization Program (NIP) (Fig. 1), which showed that HPV vaccine coverages of most Asian countries was very low [5].Whereas 83.4% and 62.5% of 15-year-old girls in Malaysia and Korea completed HPV vaccinations in 2018, respectively, vaccine coverage in Indonesia, the Philippines and Singapore ranged from just 0.1% to 0.7%.In Thailand, 91% of 12,500 girls aged 11 years in Ayutthaya province received at least 2 doses of HPV vaccine within a pilot vaccination program from 2014 to 2016, with excellent safety and high acceptability.Thereafter, HPV vaccination gradually expanded to the national level in
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.110 | 0.019 |
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".