Genotyping and Cytology Triage of High-Risk HPV DNA Positive Women for Detection of Cervical High-Grade Lesions
Bibliographic record
Abstract
OBJECTIVE: A demonstration project of primary human papillomavirus (HPV) testing was initiated in 2011 among more than 23,000 women attending routine cervical cancer screening. We examined the additional diagnostic performance of HPV genotyping for detecting disease in women with abnormal cytology. METHODS: Women aged 30 to 65 years were originally screened for HPV using Hybrid Capture II test. Women with positive results were triaged using conventional cytology, and those with atypical squamous cells of undetermined significance or worse (≥ASC-US) were referred to colposcopy. We retrospectively genotyped (Roche cobas 4800 HPV system [Roche Molecular Systems Inc, Pleasanton, CA]) cervical specimens that were HPV + with Hybrid Capture II test and extracted women's medical history postbaseline screening. We calculated positive predictive values (PPVs) and 95% confidence intervals (CIs) of triage tests to detect histologically confirmed cervical intraepithelial neoplasia of grade 2 or worse (CIN2 + ) within the first year of follow-up among women positive for HPV16, HPV18, and HPV16 and/or HPV18 as well as among those negative for HPVs 16 and 18. RESULTS: Of 1,396 HPV-positive women, 1,092 (78%) were classified as normal, 136 (10%) had CIN1, 80 (6%) had CIN2, 81 (6%) had CIN3, and 7 women had cancer throughout the entire follow-up period. Seventy CIN2 + cases were detected within the first year of follow-up. The PPV for detecting CIN2 + was 20.9% (63/239; 95% CI = 16.4-25.9) for ASC-US + cytology. In women with ASC-US + , PPVs were 31.2% (24/77; 95% CI = 21.1-42.7) for HPV16 + , 27.8% (5/18; 95% CI = 9.7-53.5) for HPV18 + , 30.8% (28/91; 95% CI = 21.5-41.3) for HPV16 + and/or HPV18 + women, and 16.6% (35/211; 95% CI = 11.8-22.3) in women testing negative for HPVs 16 and 18. CONCLUSION: Partial genotyping as an additional triage strategy to cytology can markedly improve clinical diagnostic performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".