Viral load as a predictor of the risk of cervical intraepithelial neoplasia
Bibliographic record
Abstract
HPV infections are believed to be a necessary cause of cervical cancer. Viral burden, as a surrogate indicator for persistence, may help predict risk of subsequent SIL. We used results of HPV test and cytology data repeated every 4-6 months in 2,081 women participating in a longitudinal study of the natural history of HPV infection and cervical neoplasia in São Paulo, Brazil. Using the MY09/11 PCR protocol, 473 women were positive for HPV DNA during the first 2 visits. We retested all positive specimens by a quantitative, low-stringency PCR method to measure viral burden in cervical cells. Mean viral loads and 95% CIs were calculated using log-transformed data. RRs and 95% CIs of incident SIL were calculated by proportional hazards models, adjusting for age and HPV oncogenicity. The risk of incident lesions increased with viral load at enrollment. The mean number of viral copies/cell at enrollment was 2.6 for women with no incident lesions and increased (trend p = 0.003) to 15.1 for women developing 3 or more SIL events over 6 years of follow-up. Compared to those with <1 copy per cell in specimens tested during the first 2 visits, RRs for incident SIL increased from 1.9 (95% CI 0.8-4.2) for those with 1-10 copies/cell to 4.5 (95% CI 1.9-10.7) for those with >1,000 copies/cell. The equivalent RR of HSIL for >1,000 copies/cell was 2.6 (95% CI 0.5-13.2). Viral burden appears to have an independent effect on SIL incidence. Measurement of viral load, as a surrogate for HPV persistence, may identify women at risk of developing cervical cancer precursors.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 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".