The Road Ahead for Cervical Cancer Prevention and Control
Bibliographic record
Abstract
Since the early 1950s, Papanicolaou ("Pap") cytology screening has dramatically reduced cervical cancer mortality in most high-income settings. Currently, human papillomavirus (hpv) vaccination has the greatest potential to reduce the global burden of cervical cancer and precancerous lesions. However, as the prevalence of precancerous lesions declines, maintaining cytology as the primary screening test in settings with established programs might become less efficient. A reduction in test performance (sensitivity, specificity, and positive predictive value) would lead to an increase in unnecessary colposcopy referrals. Fortunately, hpv dna testing has emerged as a suitable candidate to replace cytology. Compared with the Pap test, hpv testing is less specific but much more sensitive in detecting high-grade precancerous lesions, less prone to human error, and more reproducible across settings. Linkage of hpv vaccination and screening registries could serve the added role of monitoring vaccine efficacy. As a triage test, cytology is expected to perform with sufficient accuracy because most hpv-positive smears would contain relevant abnormalities. This approach and others-for example, hpv testing followed by genotyping-are being evaluated in large population studies and have already been recommended in some settings. Other specific biomarkers that might perform well for screening and triage include hpv E6/E7 messenger rna testing, methylation of host or viral genes, and p16(INK4a) staining. Considering the rapid pace of major discoveries and the anticipated arrival of a nonavalent hpv vaccine (currently in phase iii trials), the evidence base in this field has become an elusive target and will continue to be an obstacle for policymakers.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.059 | 0.022 |
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".