A review of data systems for assessing the impact of HPV vaccination in selected high-income countries
Bibliographic record
Abstract
The introduction of effective human papillomavirus (HPV) vaccination, screening, and treatment programs has led the World Health Organization to call for the global elimination of cervical cancer. Assessing progress toward this goal is supported through monitoring vaccination coverage and its impact. We performed a targeted review to assess the characteristics of HPV-related data systems from seven high-income countries (HICs) that represented varied approaches, including Australia, Canada, France, Italy, Scotland, Sweden, and the United States (US). Included data systems focused on preventive and early detection measures: HPV vaccination and cervical screening programs, as well as HPV-related disease outcomes. Differences were observed in approach to development of data systems, along with variation in geographical scope and methods of data collection. A challenge exists in how to best follow-up the ongoing global-scale elimination efforts in a comprehensive manner. These sources provide a wealth of information regarding the strengths and limitations of, and notable variation among, current data systems used in HICs. This review can inform improvements to existing prevention programs and the implementation of new programs in other countries, and thus support optimization of cervical cancer prevention policy.
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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 | 0.031 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".