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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".