Performance of the <scp>HPV E6</scp>/<scp>E7 mRNA</scp> Aptima <scp>HPV</scp> assay combined with partial genotyping compared with the <scp>HPV DNA</scp> Cobas 4800 <scp>HPV</scp> test for use in primary screening: Results from the <scp>CERVIVA HPV</scp> primary screening study in Ireland
Bibliographic record
Abstract
There are currently several validated HPV tests. However, longitudinal data which spans appropriate age ranges, as well as evaluation of potential screening algorithms are necessary for screening programmes choice of test. The objective of our study was to evaluate the performance of HPV mRNA and HPV DNA testing, including partial genotyping, in routine cervical screening. As part of the CERVIVA HPV Primary Screening Study, ThinPrep samples from 10 150 women were tested for HPV mRNA using the Aptima HPV assay and HPV DNA using the Cobas 4800 HPV test. HPV mRNA-positive women were further assessed with the Aptima genotyping assay for HPV 16/18/45. Baseline cytology and prospective follow-up data were collected. The performance of the two tests was examined over 42 months (to date). HPV mRNA demonstrated equivalent sensitivity to HPV DNA testing for detection of CIN2+ (93.2% [92.4-93.9] vs 92.8% [92.0-93.6], respectively) and CIN3+ (94.6% [93.8-95.3] vs 94.6% [93.8-95.3]). HPV mRNA testing had significantly higher specificity compared to HPV DNA for detection of CIN2+ (84.0% [83.5-84.5] vs 80.8% [80.2-81.4], respectively) and CIN3+ (88.44% [88.2-88.6] vs 85.62 [85.4-85.9]). The proportion of CIN2+ and CIN3+, over 3 years (42 months), in HPV-negative women was comparable for both RNA (0.20% and 0.10%) and DNA (0.22% and 0.11%). Genotyping data was comparable across both assay platforms. In the context of HPV primary screening HPV mRNA testing has potential to reduce triage tests and follow-up tests at 12 months compared to DNA testing, with no significant difference in detection of CIN2+ and CIN3+.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".