Human papillomavirus type 16 viral load measurement as a predictor of infection clearance
Bibliographic record
Abstract
Viral load measurements may predict whether human papillomavirus (HPV) type 16 infections may become persistent and eventually lead to cervical lesions. Today, multiple PCR methods exist to estimate viral load. We tested three protocols to investigate viral load as a predictor of HPV clearance. We measured viral load in 418 HPV16-positive cervical smears from 224 women participating in the Ludwig-McGill Cohort Study by low-stringency PCR (LS-PCR) using consensus L1 primers targeting over 40 known HPV types, and quantitative real-time PCR (qRT-PCR) targeting the HPV16 E6 and L1 genes. HPV16 clearance was determined by MY09/11 and PGMY PCR testing on repeated smears collected over 5 years. Correlation between viral load measurements by qRT-PCR (E6 versus L1) was excellent (Spearman's rank correlation, ρ = 0.88), but decreased for L1 qRT-PCR versus LS-PCR (ρ = 0.61). Viral load by LS-PCR was higher for HPV16 and related types independently of other concurrent HPV infections. Median duration of infection was longer for smears with high copy number by all three PCR protocols (log rank P<0.05). Viral load is inversely related to HPV16 clearance independently of concurrent HPV infections and PCR protocol.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".