A study of type-specific HPV natural history and implications for contemporary cervical cancer screening programs
Bibliographic record
Abstract
BACKGROUND: HPV testing is replacing cytology for cervical cancer screening because of greater sensitivity and superior reassurance following negative tests for the dozen HPV genotypes that cause cervical cancer. Management of women testing positive is unresolved. The need for identification of individual HPV genotypes for clinical use is debated. Also, it is unclear how long to observe persistent infections when precancer is not initially found. METHODS: In the longitudinal NCI-Kaiser Permanente Northern California Persistence and Progression (PaP) Study, we observed the clinical outcomes (clearance, progression to CIN3+, or persistence without progression) of 11,573 HPV-positive women aged 30-65 yielding 14,158 type-specific infections. FINDINGS: Risks of CIN3+ progression differed substantially by type, with HPV16 conveying uniquely elevated risk (26% of infections with seven-year CIN3+ risk of 22%). The other carcinogenic HPV types fell into 3 distinct seven-year CIN3+ risk groups: HPV18, 45 (13% of infections, risks >5%, with known elevated cancer risk); HPV31, 33, 35, 52, 58 (39%, risks >5%); and HPV39, 51, 56, 59, 68 (23%, risks <5%). In the absence of progression, HPV clearance rates were similar by type, with 80% of infections no longer detected within three years; persistence to seven years without progression was uncommon. The predictive value of abnormal cytology was most evident for prevalent CIN3+, but less evident in follow-up. A woman's age did not modify risk; rather it was the duration of persistence that was important. INTERPRETATION: HPV type and persistence are the major predictors of progression to CIN3+; at a minimum, distinguishing HPV16 is clinically important. Dividing the other HPV types into three risk-groups is worth considering.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".