PL01.2 HPV: Changing Screening Algorithms and Scaling Up Vaccination
Bibliographic record
Abstract
Since 2006, when the first human papillomavirus (HPV) vaccine was approved, there has been much progress on the prevention of diseases associated with HPV infection, particularly cervical cancer. A decade earlier, the first clinically validated HPV assay was approved as an adjunct test in cervical cancer screening. As the two cervical cancer prevention fronts, i.e., primary via vaccination and secondary via screening, progressed more or less in parallel they have begun to intersect in recent years, as it has become clear that effective deployment of these strategies requires integration of resources and planning. However, acceptance of these technologies has not been without obstacles. Up until recently, although the evidence for the superior value of HPV testing in cervical cancer screening had become unequivocal, consensus guidelines in the U.S. tended to indicate uncertainty as to the degree and strength of evidence associated with this test. This state of affairs changed in March 2012 with the simultaneous publication of the guidelines from the US Preventive Services Task Force and from the joint society consortium that involved the American Cancer Society, the American Society for Colposcopy and Cervical Pathology, and the American Society of Clinical Pathology. These guidelines placed greater emphasis on HPV testing in primary screening and discouraged unnecessary screening at younger ages and overly frequent screening. While co-testing is still favoured by the new US guidelines, other high-resource countries began to pilot or even to implement programmatically the strategy of HPV testing as the sole primary screening test, reserving Pap cytology for the triage of women who are HPV positive. The author will review the historical milestones of how HPV-based strategies have moved to centre stage in cervical cancer prevention and discuss the research directions in the area, particularly with respect to how these strategies intersect.
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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 | 0.064 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".