MassARRAY Spectrometry Is More Sensitive than PreTect HPV-Proofer and Consensus PCR for Type-Specific Detection of High-Risk Oncogenic Human Papillomavirus Genotypes in Cervical Cancer
Bibliographic record
Abstract
Type-specific detection of human papillomavirus (HPV) is indicated for better risk stratification and clinical management of women testing positive for HPV and for epidemiologic surveillance. MassARRAY spectrometry (MassARRAY; Sequenom) is a novel method for type-specific detection of 15 high-risk oncogenic HPV types: HPV-16, -18, -31, -33, -35, -39, -45, -51, -52, -56, -58, -59, -66, -68, and -73. PreTect HPV-Proofer (Proofer; Norchip) is a type-specific assay that detects E6/E7 mRNA from five high-risk oncogenic HPV types: HPV-16, -18, -31, -33, and -45. The performance of these tests for type-specific identification of HPV was assessed with cervical specimens from 192 cases of cervical cancer in comparison with consensus MY09/MY11 PCR followed by nucleotide sequencing (consensus PCR). The overall HPV detection rates were 94.8% (95% confidence interval [CI], 91.7, 97.9), 83.3% (95% CI, 78.1, 88.5), and 86.5% (95% CI, 81.7, 91.3) for MassARRAY, Proofer, and consensus PCR, respectively. All tests were negative in six (3.1%) of the 192 cases. Considering only the specimens that contained at least one of the five types targeted by Proofer, the detection rates were 96.6%, 91.4%, and 86.9% for MassARRAY, Proofer, and consensus PCR, respectively. MassARRAY detected multiple infections in 14.1%, Proofer detected multiple infections in 3.6%, and consensus PCR failed to detect any multiple infections. The agreement was highest at 86.0% (kappa = 0.76) between MassARRAY and Proofer and lowest at 81.8% (kappa = 0.69) between Proofer and consensus PCR. In conclusion, MassARRAY is a highly sensitive and accurate method for type-specific detection of oncogenic HPV in cervical cancer, with Proofer showing impressive performance.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".