Optimal Cutoff of the Hybrid Capture II Human Papillomavirus Test for Self-Collected Vaginal, Vulvar, and Urine Specimens in a Colposcopy Referral Population
Bibliographic record
Abstract
OBJECTIVE: To estimate the optimal relative light unit ratio, as a measure of viral load, of the Hybrid Capture II human papillomavirus (HPV) test in self-collected specimens for detecting cervical intraepithelial neoplasia (CIN). METHODS: Two hundred women referred for colposcopy with abnormal cytologic, self-collected vaginal and vulvar swabs and urine for HPV testing. The receiver operating characteristic (ROC) curve method was used to estimate optimal cutoffs for the Hybrid Capture II test. The reference standard was colposcopy, with directed biopsy as required. RESULTS: The estimated optimal cutoffs of the relative light unit ratio for detecting CIN 2 or higher for urine, vulvar, and vaginal samples gave sensitivities of 72.4%, 82.8%, and 89.0% and specificities of 57.0%, 52.1%, and 55.9%, respectively. At the manufacturer's recommended 1.0 cutoff, sensitivities were 44.8%, 62.1%, and 86.2% for urine, vulvar, and vaginal samples, with specificities of 69.7%, 62.7%, and 53.5%, respectively. The likelihood ratios (likelihood of being truly positive after a positive test result) were similar for the optimal and the 1.0 cutoff. CONCLUSIONS: The ROC curve methods did not improve the overall diagnostic accuracy of the Hybrid Capture II test compared with the 1.0 relative light unit ratio cutoff.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".