E6/E7 Similarity to Human Papillomavirus Prototypes and Performance of HPV Testing by Cobas 4800 HPV Test and Anyplex II HPV HR
Bibliographic record
Abstract
Human papillomavirus (HPV) genotype classification relies on DNA sequence similarity to reference (prototype) sequences. Most HPV assays used for cervical cancer screening were clinically validated against European HPV prototypes. However, the impact of HPV sequence polymorphisms on test performance remains unexplored. We evaluated whether sequence variation in E6/E7 relative to HPV prototypes affects test performance by analyzing cervicovaginal samples from 990 women enrolled (2019-2022) across Belgium, Portugal, Brazil and Ecuador. Samples were tested using cobas, Anyplex, and next-generation sequencing (Ampliseq/Ion Torrent targeting E6/E7). Sequence variation was defined as the proportion of single nucleotide polymorphisms across E6/E7 relative to reference sequence. Sequence variation was, on average, higher in HPV-negative than HPV-positive samples for HPV16 (0.46% vs 0.13%) and HPV18 (0.44% vs 0.37%) using cobas. Similar patterns were observed with Anyplex (HPV16: 0.78% vs 0.13%, HPV33: 0.66% vs 0.40%, HPV58: 0.79% vs 0.53, and HPV66: 1.14% vs 0.25%). For HPV45, sequence variation was, on average, higher in HPV-positive than HPV-negative samples when tested with Anyplex (0.87% vs 0.43%). For HPV types 18, 31, 35, 39, 51, 52, 56, 59 and 68, the mean sequence variation was similar between HPV-negative and HPV-positive samples using Anyplex. Our findings show that sequence variation relative to prototypes may impact test performance.
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 | 0.004 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".