Prevalence of high-risk human papilloma virus genotypes and associated risk of cervical precancerous lesions in a large U.S. screening population: Data from the ATHENA trial
Bibliographic record
Abstract
OBJECTIVE: We assessed the age-related prevalence of high risk human papillomavirus (HR-HPV) genotypes and the genotype-associated risk for high-grade cervical intraepithelial neoplasia (CIN) in a large U.S. screening population. METHODS: A total of 40,901 women aged ≥25 years were screened with liquid-based cytology and HPV testing in the ATHENA (Addressing the Need for Advanced HPV Diagnostics) trial. Genotyping was performed using the LINEAR ARRAY HPV Genotyping Test. RESULTS: HPV16 was the most prevalent genotype in all age groups, ranging from 3.5% to 0.8% in women aged 25-29 and ≥50 years, respectively. The next most prevalent genotypes were HPV52, HPV31 and HPV18. In the overall population, HPV16 conferred the greatest absolute risk of ≥CIN3 both in women aged 25-29 and ≥30 years (14.2% and 15.1%, respectively) followed by HPV31 (8.0% and 7.9%), HPV52 (6.7% and 4.4%) and HPV18 (2.7% and 9.0%). Similar trends were seen in women with negative cytology. The percent positivity increased markedly with disease progression for HPV16 and HPV18 which were responsible for 45.6% and 8.4% of ≥CIN3, respectively. Of note, HPV 18 was responsible for 50% of adenocarcinoma in situ (AIS) and 50% of invasive cancer cases. CONCLUSIONS: HPV16 played a major role in the development of ≥CIN3 irrespective of age, supporting the identification of HPV16 in primary screening for all women. Identification of HPV18 is also warranted, given its significant contribution to AIS and cancer. Identification of non-16/18 genotypes as a pool should provide sufficient information for screening.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".