Clinical Performance of the PreTect HPV-Proofer E6/E7 mRNA Assay in Comparison with That of the Hybrid Capture 2 Test for Identification of Women at Risk of Cervical Cancer
Bibliographic record
Abstract
Human papillomavirus (HPV) DNA testing has a higher clinical sensitivity than cytology for the detection of high-grade cervical intraepithelial neoplasia or worse (CIN 2+). However, an improvement in specificity would be desirable. As malignant transformation is induced by HPV E6/E7 oncogenes, detection of E6/E7 oncogene activity may improve specificity and be more predictive of cervical cancer risk. The PreTect HPV-Proofer assay (Proofer; Norchip) detects E6/E7 mRNA transcripts from HPV types 16, 18, 31, 33, and 45 with simultaneous genotype-specific identification. The clinical performance of this assay was assessed in a cross-sectional study of women referred for colposcopy in comparison with the Hybrid Capture 2 (HC2; Qiagen) test, which detects DNA of 13 high-risk oncogenic HPV types collectively. Cervical specimens were collected in PreservCyt, and cytology was performed using the ThinPrep method (Hologic). The samples were processed for HPV detection with Proofer and HC2 and genotyping with the Linear Array method (Roche Molecular Systems). Histology-confirmed CIN 2+ served as the disease endpoint to assess the clinical performance of the tests. A total of 1,551 women were studied, and of these, 402 (25.9%) were diagnosed with CIN 2+ on histology. The Proofer assay showed a sensitivity of 78.1% (95% confidence interval [CI], 74.1 to 82.1) versus 95.8% (95% CI, 93.8 to 97.8) for HC2 (P < 0.05) and a specificity of 75.5% (95% CI, 73.0 to 78.0) versus 39.6% (95% CI, 36.8 to 42.4), respectively (P < 0.05). The lower sensitivity and higher specificity of Proofer for detection of CIN 2+ can be attributed to the fact that this test detects the expression of E6/E7 genes beyond a threshold from a limited number of oncogenic HPV types. In conclusion, Proofer is more specific than HC2 in identifying women with CIN 2+ but has a lower sensitivity.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".