HPV Extended Genotyping to Triage Abnormal Cervical Cancer Screens—Balancing the Harms and Benefits of an Additional Triage Test before Direct Colposcopy Referral
Bibliographic record
Abstract
The Netherlands' cervical cancer screening program transitioned to primary human papillomavirus (HPV) screening in 2017. After the introduction of HPV-based screening, the country saw increases in colposcopy referral rates and detections of low-grade lesions. In July 2022, genotyping was introduced, and those with borderline or mild dyskaryotic (BMD) cytologic abnormalities were only referred to colposcopy if positive for HPV type 16 or 18, and repeat screening otherwise. In this article, various strategies using extended genotyping (HPV16/18/31/33/45/52/58) as a triage test after an abnormal screen were explored using data from HPV-positive participants with normal or BMD cytology in the Population-Based Screening Study Amsterdam (POBASCAM) trial. The authors assessed positive and negative predictive values and colposcopy referral rates for each strategy using extended genotyping to triage women to either direct referral to colposcopy or repeat screening. Direct referral did not meet positive and negative predictive value thresholds for efficiency for any strategies. However, the authors note that direct referral may nonetheless be useful among those with BMD due to minimal increases in colposcopy referrals and concerns of loss to follow-up at repeat screening. These findings demonstrate the potential utility of extended genotyping as a triage test in primary HPV screening programs. The results should be considered alongside the fact that referral to repeat screening may result in loss of engagement of women who need treatment to prevent invasive cancer. See related article by Kroon et al., p. 1037.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".