Eurogin roadmap 2017: Triage strategies for the management of <scp>HPV</scp>‐positive women in cervical screening programs
Bibliographic record
Abstract
Cervical cancer screening will rely, increasingly, on HPV testing as a primary screen. The requirement for triage tests which can delineate clinically significant infection is thus prescient. In this EUROGIN 2017 roadmap, justification behind the most evidenced triages is outlined, as are challenges for implementation. Cytology is the triage with the most follow-up data; the existence of an HR-HPV-positive, cytology-negative group presents a challenge and retesting intervals for this group (and choice of retest) require careful consideration. Furthermore, cytology relies on subjective skills and while adjunctive dual-staining with p16/Ki67 can mitigate inter-operator/-site disparities, clinician-taken samples are required. Comparatively, genotyping and methylation markers are objective and are applicable to self-taken samples, offering logistical advantages including in low and middle income settings. However, genotyping may have diminishing returns in immunised populations and type(s) included must balance absolute risk for disease to avoid low specificity. While viral and cellular methylation markers show promise, more prospective data are needed in addition to refinements in automation. Looking forward, systems that detect multiple targets concurrently such as next generation sequencing platforms will inform the development of triage tools. Additionally, multistep triage strategies may be beneficial provided they do not create complex, unmanageable pathways. Inevitably, the balance of risk to cost(s) will be key in decision making, although defining an acceptable risk will likely differ between settings. Finally, given the significant changes to cervical screening and the variety of triage strategies, appropriate education of both health care providers and the public is essential.
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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 | 0.030 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".