Optimizing secondary prevention of cervical cancer: Recent advances and future challenges
Bibliographic record
Abstract
Although human papillomavirus (HPV) vaccines offer enormous promise for the ultimate control and possible elimination of cervical cancer, barriers to uptake and coverage of the vaccine both in high- and low/middle-income settings mean that advances in secondary prevention continue to be essential to prevent unnecessary deaths and suffering from cervical cancer for decades to come. While cytology (the Pap smear) has reduced cervical cancer incidence and prevalence in jurisdictions where it has been systematically implemented in population-based programs-mainly in high-income settings-limitations inherent to this method, and to program delivery, leave many women still vulnerable to cervical cancer. Recent evidence has confirmed that screening based on HPV testing prevents more invasive cervical cancer and precancerous lesions, and offers innovative options such as self-collection of specimens to improve screening uptake broadly. In this paper, we review key advances, future opportunities, and ongoing challenges for secondary prevention of cervical cancer using HPV-based testing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".