Attribution of 12 High-Risk Human Papillomavirus Genotypes to Infection and Cervical Disease
Bibliographic record
Abstract
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.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.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.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.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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".