Primary hrHPV DNA Testing in Cervical Cancer Screening: How to Manage Screen-Positive Women? A POBASCAM Trial Substudy
Bibliographic record
Abstract
BACKGROUND: High-risk human papillomavirus (hrHPV) testing has higher sensitivity but lower specificity than cytology for cervical (pre)-cancerous lesions. Therefore, triage of hrHPV-positive women is needed in cervical cancer screening. METHODS: A cohort of 1,100 hrHPV-positive women, from a population-based screening trial (POBASCAM: n = 44,938; 29-61 years), was used to evaluate 10 triage strategies, involving testing at baseline and six months with combinations of cytology, HPV16/18 genotyping, and/or repeat hrHPV testing. Clinical endpoint was cervical intraepithelial neoplasia grade 3 or worse (CIN3(+)) detected within four years; results were adjusted for women not attending repeat testing. A triage strategy was considered acceptable, when the probability of no CIN3(+) after negative triage (negative predictive value, NPV) was at least 98%, and the CIN3(+) risk after positive triage (positive predictive value, PPV) was at least 20%. RESULTS: Triage at baseline with cytology only yielded an NPV of 94.3% [95% confidence interval (CI), 92.0-96.0] and a PPV of 39.7% (95% CI, 34.0-45.6). An increase in NPV, against a modest decrease in PPV, was obtained by triaging women with negative baseline cytology by repeat cytology (NPV 98.5% and PPV 34.0%) or by baseline HPV16/18 genotyping (NPV 98.8% and PPV 28.5%). The inclusion of both HPV16/18 genotyping at baseline and repeat cytology testing provided a high NPV (99.6%) and a moderately high PPV (25.6%). CONCLUSIONS: Triaging hrHPV-positive women by cytology at baseline and after 6 to 12 months, possibly in combination with baseline HPV16/18 genotyping, seems acceptable for cervical cancer screening. IMPACT: Implementable triage strategies are provided for primary hrHPV screening in an organized setting.
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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 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".