Performance of proex c and pretect hpv‐proofer e6/e7 mrna tests in comparison with the hybrid capture 2 hpv dna test for triaging ascus and lsil cytology
Bibliographic record
Abstract
The clinical usefulness of the ProEx C (Becton Dickinson) and PreTect HPV-Proofer E6/E7 mRNA tests (Proofer; Norchip) for the triage of ASCUS and LSIL cytology was determined in comparison with the Hybrid Capture 2 HPV DNA test (HC2; Qiagen). The study population consisted of women with a history of abnormal cytology referred to colposcopy. Histology-confirmed CIN 2+ served as the disease endpoint. The study was based on 1,360 women (mean age 30.7 years), of whom 380 had CIN 2+. Among 315 with ASCUS (CIN 2+, n = 67), the sensitivities of ProEx C, Proofer, and HC2 to detect CIN 2+ were, 71.6, 71.6, and 95.5%, respectively, with a corresponding specificity of 74.6, 74.2, and 35.1%. Among 363 with LSIL (CIN 2+, n = 108), the sensitivities of ProEx C, Proofer, and HC2 were, 67.6, 74.1, and 96.3%, respectively, with a corresponding specificity of 60, 68.2, and 18.4%. Among 225 HC2-positive ASCUS (CIN 2+, n = 64), 105 tested positive by ProEx C, reducing colposcopy referral by 53.3% and detecting 71.9% of CIN 2+; Proofer was positive in 112/225, reducing colposcopy referral by 50.2% and detecting 75.0% of CIN 2+. Among 312 HC2-positive LSIL (CIN 2+, n = 104), 160 tested positive by ProEx C, reducing coloposcopy referral by 48.7% and detecting 66.3% of CIN 2+; Proofer was positive in 159/312, reducing colposcopy referral by 49.0% and detecting 75.0% of CIN 2+. In conclusion, both ProEx C and Proofer have a similar performance profile with a significantly higher specificity but lower sensitivity than HC2 for the detection of CIN 2+. Consequently, although they can reduce colposcopy referral, they will miss a proportion of CIN 2+ cases. This is a major limitation and should be taken into account if these tests are considered for ASCUS or LSIL triage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".