Integrating Human Papillomavirus Vaccination in Cervical Cancer Control Programmes
Bibliographic record
Abstract
Screening with Pap cytology has substantially reduced cervical cancer morbidity and mortality during the last 50 years in high-income countries. Unfortunately, in resource-poor countries, Pap screening has either not been effectively implemented or has failed to reduce cervical cancer rates. Cervical cancer in these countries thus remains a major public health problem. Infection with certain human papillomavirus (HPV) types is now recognized as a necessary cause of this disease and has led to new preventive strategies for cervical cancer. Testing for HPV DNA of oncogenic types is gaining increasing interest and application in cervical cancer screening. It has much greater sensitivity and only slightly lower specificity than Pap cytology. Molecular-based screening will be of particular clinical value in the post-vaccine era in which cervical disease will be a rare event and may escape cytology-based detection. As a primary screening test followed by Pap triage of HPV-positive cases, HPV testing has the potential to improve the overall quality of screening programmes, thus allowing for increased testing intervals, which would lower program costs with acceptable safety. Prophylactic vaccines against the 2 leading oncogenic HPV types (16 and 18) have been recently licensed. In large clinical trials, they have shown excellent safety and nearly 100% efficacy in preventing persistent infections and the cervical pre-cancers due to vaccine HPV types 16 and 18. Combining modern screening techniques and universal prophylactic HPV vaccination is likely to produce the most advanced and cost-effective preventive strategy to fight cervical cancer worldwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".