Optimizing the hybrid capture II human papillomavirus test to detect cervical intraepithelial neoplasia*1
Bibliographic record
Abstract
OBJECTIVE: To estimate the optimal relative light unit ratio, and correspondingly viral load, of the hybrid capture II oncogenic human papillomavirus deoxyribonucleic acid test for detecting cervical intraepithelial neoplasia (CIN). METHODS: Women with abnormal cytology were referred for colposcopy, and a cervical swab or brush specimen was obtained for human papillomavirus testing. Sensitivities, specificities, and likelihood ratios of different relative light unit ratio cutoffs were calculated using a reference standard of colposcopy or biopsy of either CIN II+ (CIN II, III, or carcinoma), or CIN I+ (CIN I, CIN II+). The receiver operating characteristic curve was used to estimate optimal test-positive cutoff points for the hybrid capture II test. RESULTS: CIN II+ was found in 18.7% (98 of 524) and CIN I in 10.5% (55 of 524) of the women. The optimal relative light unit ratio was 15.56, giving a sensitivity and specificity of 82.7% and 73.2% for CIN II+, and 74.2% and 77.8% for CIN I+. In a stratified analysis, a higher relative light unit cutoff (15.19) optimized sensitivity and specificity for CIN II+ (sensitivity 81.8%, specificity 51.5%) for women with low-grade squamous intraepithelial lesions cytology, whereas the optimal cutoff was 2.36 (sensitivity 100%, specificity 73.0%) for women with atypical squamous cells of undetermined significance, yielding referral rates of 53.3% and 28.7%, respectively. Use of a swab or brush was not associated with the level of human papillomavirus detected (P >.05). CONCLUSION: Use of a higher cutoff for the relative light unit ratio (higher viral load) of the hybrid capture II test may improve the management of women, especially those with low-grade squamous intraepithelial lesions cytology.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".