Scientific approaches toward improving cervical cancer elimination strategies
Bibliographic record
Abstract
At the 2023 EUROGIN workshop scientific basis for strategies to accelerate the elimination of cervical cancer and its causative agent, human papillomavirus (HPV) were reviewed. Although some countries have reached key performance indicators toward elimination (>90% of girls HPV vaccinated and >70% of women HPV screened), most are yet to reach these targets, implying a need for improved strategies. Gender-neutral vaccination, even with moderate vaccination coverage was highlighted as a strategy to achieve elimination more rapidly. It is more resilient against major disturbances in vaccination delivery, such as what happened during the coronavirus pandemic. Further, an analysis of ethical/legal issues indicated that female-restricted vaccination is problematic. Extended catch-up of vaccination with concomitant screening, and outreach to vulnerable groups were highlighted. Although birth cohorts with high coverage of HPV vaccination at school are protected against HPV, and HPVs have a very low reproductive rate in women above age 35, adult women below age 30 have inadequate direct protection. In addition to herd protection from gender-neutral vaccination, this group can be protected by offering concomitant catch-up HPV vaccination and HPV screening. Furthermore, hepatitis B vaccination experiences indicate that elimination cannot be achieved without prioritizing vulnerable/migrant populations. The long-lasting durability of vaccination-induced antibody responses suggests prolonged protection with HPV vaccines when adequately administrated. Finally, cost-effectiveness modelling suggests that high-coverage HPV vaccination in multiple population segments will be resource-saving due to reduced need for screening. In summary, the workshop found that strategically optimal deployment of vaccination will accelerate elimination of HPV and cervical cancer.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".