High Level of Correlation of Human Papillomavirus-16 DNA Viral Load Estimates Generated by Three Real-time PCR Assays Applied on Genital Specimens
Bibliographic record
Abstract
Human papillomavirus-16 (HPV-16) viral load could be a biomarker predictive of the presence of high-grade cervical lesions. Recently, several real-time PCR assays have been developed to accurately measure HPV-16 viral load. However, results from various reports using these assays cannot be compared because interassay test correlation has not been documented. The variability of HPV-16 DNA quantitation was assessed by comparing three real-time PCR assays (HPV-16 L1, HPV-16 E6, and HPV-16 E6 PG) applied on 144 genital samples (125 cervicovaginal lavages and 19 specimens collected using vaginal tampons) obtained from 84 women (66 HIV seropositive and 18 HIV seronegative). Correlation was greater between the HPV-16 E6 assays [correlation coefficient (rho) = 0.92] than between each E6 assay and HPV-16 L1 assay (rho = 0.83 and 0.84, respectively). The median HPV-16 copies measured by HPV-16 E6 PG (14,609 HPV-16 copies/2 muL sample) and HPV-16 E6 (18,846 HPV-16 copies/2 muL) were similar (P = 0.27) but were both greater than the median HPV-16 copies measured with the L1 assay (4,124 HPV-16 copies/2 muL; P < 0.001). Correlations between HPV-16 E6 assays were similar for samples containing non-European (rho = 0.93) or European (rho = 0.95) variants. However, the correlation between HPV-16 L1 and HPV-16 E6 PG or HPV-16 E6 was lower for specimens containing non-European variants (rho = 0.80 and 0.76, respectively) compared with specimens containing European variants (rho > 0.85). HPV-16 DNA quantity estimated with the three assays was comparable although lower with the HPV-16 L1 assay. The level of correlation depended on viral polymorphism, viral load, and cervical disease status.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".