Universal HPV Vaccination – a Global Prerogative
Bibliographic record
Abstract
Worldwide cancer incidence is increasing, with viral infections including human papillomavirus (HPV) responsible for a significantly higher number of cancer deaths in low- and middle-income countries (LMICs) when compared to high income countries. Globally, in 2015, there were 72 national HPV vaccination programmes, and 39 demonstration or pilot programmes. Despite HPV’s impact on both sexes, for examples in malignancies such as oropharyngeal cancer (whose incidence is increasing across the world) few countries have a gender-neutral vaccination policy. Herd protection and cost-effectiveness are important considerations in potential extension of vaccination to males and while there is some suggestion that a targeted vaccination programmes for “high risk” groups, such as men-who-have-sex-with-men (MSM) may be preferable, a universal vaccination approach is the best solution to protect both men and women from HPV-related cancer and sexually transmitted disease. Higher incidence of certain HPV-related male cancers, lack of effective treatment, high prevalence of HIV, attitudes to MSM and sexual orientation, all support a universal vaccination strategy for LMICs. Thus, policy-makers and healthcare providers in LMICs need to take timely decisions to “prevent the preventable” by providing vaccination for both girls and boys.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".