HPV genotype-specific distribution and attributable risk in cervical intraepithelial neoplasia in a referral population with a history of LSIL.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: CINtec PLUS and cobas HPV tests (Roche) were previously ascertained for triaging an LSIL referral population [1]. As part of this study, genotype-specific distribution and attributable risk of high-risk (HR)-HPV in cervical intraepithelial neoplasia (CIN) were determined. METHODS: Archived cervical specimens in ThinPrep PreservCyt (Hologic Inc) from the LSIL referral population (n= 533) were genotyped using the Anyplex II HPV HR test (Anyplex, Seegene Inc). Since the study specimens had been in storage in ambient temperature for 31-47 months since collection, Anyplex results were compared with that of the initial cobas testing of fresh specimens to validate the suitability and stability of specimens for the present study. RESULTS: Overall, Anyplex test was positive in 63% (336/533) vs. 55.7% (297/533) for cobas test. Anyplex test performed identical to cobas test identifying 93.2% (82/88) of ⩾CIN2/adenocarcinoma in situ (AIS). Anyplex test detected genotypes 16/18 in 15.7% (36/230) ⩽CIN1 vs. 45.5% (40/88) ⩾CIN2/AIS; the corresponding figures were 13.5% (31/230) and 45.5% (40/48) for the cobas test. Genotype 16 showed increasing attribution, 13.2% in CIN1, 27.1% in CIN2 and 40% in CIN3/AIS. Of the 12 other high-risk (OHR) types collectively identified by cobas, Anyplex test specifically detected, in decreasing order, genotypes 51, 31, 35, 56, 39, and 45 as the most frequent types, often in multiple-type infections, in 64.8% ⩾CIN2. Regardless, estimated attribution was evident for each of the 12 OHR types in ⩾CIN2. Multiple-type infections were more frequent than single-type infections in all CIN grades. CONCLUSIONS: Attributable risk of all HR-HPV genotypes targeted by both Anyplex and cobas tests was evident in ⩾CIN2/AIS Testing for these genotypes in HPV primary cervical screening and cytology triage could identify those at increased risk of cervical cancer and also be beneficial in the management of LSIL referral populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".