Prevalence of human papillomavirus and cervical intraepithelial neoplasia in China: A pooled analysis of 17 population‐based studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High-risk (HR) human papillomavirus (HPV) prevalence has been shown to correlate well with cervical cancer incidence rates. Our study aimed to estimate the prevalence of HR-HPV and cervical intraepithelial neoplasia (CIN) in China and indirectly informs on the cervical cancer burden in the country. A total of 30,207 women from 17 population-based studies throughout China were included. All women received HPV DNA testing (HC2, Qiagen, Gaithersburg, MD), visual inspection with acetic acid and liquid-based cytology. Women positive for any test received colposcopy-directed or four-quadrant biopsies. A total of 29,579 women had HR-HPV testing results, of whom 28,761 had biopsy confirmed (9,019, 31.4%) or assumed (19,742, 68.6%) final diagnosis. Overall crude HR-HPV prevalence was 17.7%. HR-HPV prevalence was similar in rural and urban areas but showed dips in different age groups: at age 25-29 (11.3%) in rural and at age 35-39 (11.3%) in urban women. In rural and urban women, age-standardized CIN2 prevalence was 1.5% [95% confidence interval (CI): 1.4-1.6%] and 0.7% (95% CI: 0.7-0.8%) and CIN3+ prevalence was 1.2% (95% CI: 1.2-1.3%) and 0.6% (95% CI: 0.5-0.7%), respectively. Prevalence of CIN3+ as a percentage of either all women or HR-HPV-positive women steadily increased with age, peaking in 45- to 49-year-old women. High prevalence of HR-HPV and CIN3+ was detected in both rural and urban China. The steady rise of CIN3+ up to the age group of 45-49 is attributable to lack of lesion removal through screening. Our findings document the inadequacy of current screening in China while indirectly raising the possibility that the cervical cancer burden in China is underreported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it