Monitoring the impact of HPV vaccine in males—Considerations and challenges
Bibliographic record
Abstract
In this article, we examine the issues involved if national or sub-national programs are considering extending post HPV vaccine introduction monitoring to include males. Vaccination programs are now being extended to include males in some countries, in order to improve population level HPV infection control and to directly prevent HPV-related disease in males such as anogenital warts and anal cancers. Coverage and adverse events surveillance are essential components of post-vaccination monitoring. Monitoring the impact of vaccination on HPV infection and disease in men raises some similar challenges to monitoring in females, such as the long time frame until cancer outcomes, and also different ones given that genital specimens suitable for monitoring HPV prevalence are not routinely collected for other diagnostic or screening purposes in males. Thus, dedicated surveillance strategies must be designed; the framework of these may be country-specific, dependent upon the male population that is offered vaccination, the health care infrastructure and existing models of disease surveillance such as STI networks. The primary objective of any male HPV surveillance program will be to document changes in the prevalence of HPV infection and disease due to vaccine targeted HPV types occurring post vaccination. The full spectrum of outcomes to be considered for inclusion in any surveillance plan includes HPV prevalence monitoring, anogenital warts, potentially pre-cancerous lesions such as anal squamous intraepithelial lesions (SIL), and cancers. Ideally, a combination of short term and long term outcome measures would be included. Surveillance over time in specific targeted populations of men who have sex with men and HIV-infected men (populations at high risk for HPV infection and associated disease) could be an efficient use of resources to demonstrate impact.
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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".