Comparison of Alinity m HPV and cobas HPV assays on cervical specimens in diverse storage media
Bibliographic record
Abstract
OBJECTIVE: To assess the concordance of high-risk HPV (HR-HPV) testing with the Alinity assay on cervical samples collected with diverse collection/storage protocols (ThinPrep, SurePath, Cervicollect) and to assess inter-assay concordance of HR-HPV testing of cervical cell specimens with Alinity m HR HPV assay (Alinity) vs cobas® 4800 HPV assay (cobas). METHODS: Specimens were obtained from 560 women attending a Women's Health clinic. Two specimens were obtained from each woman with combinations of two of the three collection devices and aliquots were tested by the two assays. RESULTS: Alinity showed an agreement of 93.9%, Kappa = 0.89 (263/280) between ThinPrep and SurePath specimens; 97.5%, Kappa = 0.95 (347/356) and 92.9%, Kappa = 0.85 (104/112) between ThinPrep and SurePath aliquots taken before or after cytology processing, respectively. Cervi-Collect specimens showed an agreement of 94.6%, Kappa = 0.89 (265/280) with ThinPrep specimens. Compared to cobas, Alinity showed agreements of 94.3%, Kappa = 0.88 (395/419) and 91.8%, Kappa = 0.82 (257/280) between ThinPrep and SurePath specimens, respectively. Alinity and cobas detected genotypes 16/18 and other high-risk HPV types at similar rates and showed similar correlations with cytology grades. CONCLUSIONS: Compared to cobas, Alinity performed equally well for detecting HPV in cervical specimens obtained with ThinPrep and SurePath. The Cervi-Collect device compared well to the other collection methods. Alinity is a reliable assay for simultaneous detection of HPV-16/18 and other high-risk genotypes in cervical specimens.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".