Human papillomavirus genotype concordance between Anyplex II HPV28 and linear array HPV genotyping test in anogenital samples
Bibliographic record
Abstract
Anyplex II HPV-28 (HPV-28) can detect individually 28 HPV genotypes. We assessed the agreement between linear array HPV genotyping (LA-HPV) and HPV-28 for detection of 27 HPV genotypes in 410 stored anogenital samples (75 anal samples, 335 physician-collected cervical samples) collected over 5 years from 410 individuals (13 men, 397 women), including 202 HIV-seropositive individuals. HPV DNA was detected in 393 (95.9%, 95% confidence interval [CI]: 93.4-97.4) and 382 (93.2%, 95% CI: 90.3-95.3) samples with HPV-28 and LA-HPV (p = 0.13), respectively, for a good agreement of 96.3% (κ = 0.65). Of the 10503 HPV typing results, 10195 (780 positive, 9577 negative) were concordant, for an agreement of 97.1% (95% CI: 96.7-97.4) and an excellent of κ = 0.82 (95% CI: 0.80-0.84). The mean type-specific concordance for 27 genotypes was 97.0%, 95% CI: 95.8-98.5 (κ = 0.86 ± 0.07, 95% CI: 0.83-0.88). Excellent agreement was obtained individually for all high-risk genotypes (κ = 0.81-0.97) and for most other genotypes except for types 42, 44, 54, 68, and 69. The mean number of types per sample in discordant samples detected with LA-HPV (3.0, 95% CI: 2.7-3.4) was greater than in concordant samples (1.4, 95% CI: 1.3-1.5; p< 0.001). In conclusion, HPV-28 compared favorably with LA-HPV, but was more frequently positive for HPV42 and HPV68.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".