Detection of cervical precancerous lesions with Aptima HPV assays using SurePath preservative fluid specimens
Bibliographic record
Abstract
SurePath specimens from women referred to colposcopy were treated with Aptima Transfer Solution (ATS) before testing in Aptima HPV (AHPV) and Aptima HPV 16, 18/45 (AHPV-GT) assays. Untreated SurePath specimens were tested with the cobas HPV test. PreservCyt specimens were assessed for cytology and tested with AHPV. High-grade cervical intraepithelial neoplasia lesions served as the reference standard. Excellent agreement (95.5%; k=0.91) was observed for ATS-treated SurePath specimens between Tigris and Panther systems and between the PreservCyt and ATS-treated SurePath specimens (91.1%, k=0.81) with the AHPV assay on Tigris. Agreement between the AHPV and cobas assays with SurePath specimens was substantial (89.9%, k=0.80). AHPV sensitivity for CIN2+(n=147) was 91.2% for SurePath and PreservCyt. Cobas HPV sensitivity was 93.9% for SurePath specimens. AHPV testing of SurePath specimens was more specific (59.4%) than cobas (54.7%) (p<0.001). Detection and genotyping showed similar absolute and relative risks. ATS-treated SurePath specimens tested with AHPV and AHPV-GT assays showed similar performance with greater specificity than cobas HPV on SurePath specimens. Similar overall results were seen using a CIN3 disease endpoint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".