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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".