Attribution of 12 High-Risk Human Papillomavirus Genotypes to Infection and Cervical Disease
Bibliographic record
Abstract
BACKGROUND: We estimated the prevalence and incidence of 14 human papillomavirus (HPV) types (6/11/16/18/31/33/35/39/45/51/52/56/58/59) in cervicovaginal swabs, and the attribution of these HPV types in cervical intraepithelial neoplasia (CIN), and adenocarcinoma in situ (AIS), using predefined algorithms that adjusted for multiple-type infected lesions. METHODS: A total of 10,656 women ages 15 to 26 years and 1,858 women ages 24 to 45 years were enrolled in the placebo arms of one of three clinical trials of a quadrivalent HPV vaccine. We estimated the cumulative incidence of persistent infection and the proportion of CIN/AIS attributable to individual carcinogenic HPV genotypes, as well as the proportion of CIN/AIS lesions potentially preventable by a prophylactic 9-valent HPV6/11/16/18/31/33/45/52/58 vaccine. RESULTS: The cumulative incidence of persistent infection with ≥1 of the seven high-risk types included in the 9-valent vaccine was 29%, 12%, and 6% for women ages 15 to 26, 24 to 34, and 35 to 45 years, respectively. A total of 2,507 lesions were diagnosed as CIN or AIS by an expert pathology panel. After adjusting for multiple-type infected lesions, among women ages 15 to 45 years, these seven high-risk types were attributed to 43% to 55% of CIN1, 70% to 78% of CIN2, 85% to 91% of CIN3, and 95% to 100% of AIS lesions, respectively. The other tested types (HPV35/39/51/56/59) were attributed to 23% to 30% of CIN1, 7% to 14% of CIN2, 3% to 4% of CIN3, and 0% of AIS lesions, respectively. CONCLUSIONS: Approximately 85% or more of CIN3/AIS, >70% CIN2, and approximately 50% of CIN1 lesions worldwide are attributed to HPV6/11/16/18/31/33/45/52/58. IMPACT: If 9-valent HPV vaccination programs are effectively implemented, the majority of CIN2 and CIN3 lesions worldwide could be prevented, in addition to approximately one-half of CIN1.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".