Prevalence of High-Risk Human Papillomavirus by Subtypes Among Rural Women Aged 35–64 Years — Guangzhou City, Guangdong Province, China, 2019–2021
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
What is already known about this topic?: Little is known about the infection pattern for high-risk human papillomavirus (hrHPV) subtypes in rural areas in southern China. What is added by this report?: The prevalence of HPV-16, 18, and the other 12 hrHPV subtypes were 0.71%, 0.34%, and 4.50%, respectively, among rural women in Guangzhou. The prevalence of HPV-16 and the other 12 hrHPV subtypes increased with age, but there was no evident age trend for HPV-18 prevalence. What are the implications for public health practice?: Epidemiological characteristics of hrHPV prevalence in rural Guangzhou should be considered to identify high-risk populations of hrHPV infection and determine follow-up strategies.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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