The Risk Factors for Cervical Cytological Abnormalities Among Women Infected With Non-16/18 High-Risk Human Papillomavirus: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: High-risk human papillomavirus (hrHPV) infection is a necessary cause of almost all cervical cancers. Relative to hrHPV 16/18 infection, non-16/18 hrHPV infection is of less concern. However, the increasing prevalence of non-16/18 hrHPV infections has become an important public health issue. The early identification and treatment of cervical cytological abnormalities in women infected with non-16/18 hrHPV reduces the incidence of cervical cancer. To date, no study has examined the risk factors for cytological abnormalities in this high-risk population. OBJECTIVE: This population-based, cross-sectional study aimed to identify the risk factors for cervical cytological abnormalities in women infected with non-16/18 hrHPV. METHODS: A total of 314,587 women from the general population were recruited for cervical cancer screening at 136 primary care hospitals in Xiangyang, China. Of these, 311,604 women underwent HPV genotyping, and 17,523 non-16/18 hrHPV-positive women were referred for cytological screening according to the screening program. A logistic regression model was used to assess the risk factors for cytological abnormalities among these non-16/18 hrHPV-positive women. A separate analysis was performed to determine the factors influencing high-grade cytological abnormalities. RESULTS: The non-16/18 hrHPV infection rate was 5.88% (18,323/311,604), which was 3-fold higher than that of hrHPV 16/18 (6068/311,604, 1.95%). Among the non-16/18 hrHPV-positive women who underwent ThinPrep cytologic test, the overall prevalence rates of cervical cytological abnormalities and high-grade cytological abnormalities were 13.46% (2359/17,523) and 1.18% (206/17,523), respectively. Multivariate logistic regression analysis revealed that women with middle or high school educational attainment were at a higher risk of having cytological abnormalities than those who received primary education (odds ratio [OR] 1.31, 95% CI 1.17-1.45; P<.001, and OR 1.32, 95% CI 1.14-1.53; P<.001, respectively). Living in rural areas (OR 2.58, 95% CI 2.29-2.90; P<.001), gravidity ≥3 (OR 2.77, 95% CI 1.19-6.45; P=.02), cervix abnormalities detected in pelvic examination (OR 1.22, 95% CI 1.11-1.34; P<.001), and having a cervical cancer screening 3 years ago (OR 0.79, 95% CI 0.62-1.00; P=.048) were associated with cytological abnormalities. The risk factors for high-grade cytological abnormalities included middle school education (OR 1.45, 95% CI 1.07-1.98; P=.02), living in rural regions (OR 1.52, 95% CI 1.10-2.10; P=.01), and cervix abnormality (OR 1.72, 95% CI 1.30-2.26; P<.001). CONCLUSIONS: The dominant epidemic of non-16/18 hrHPV infection is revealed in Chinese women. Multiple risk factors for cervical cytological abnormalities have been identified in women infected with non-16/18 hrHPV. These findings can provide important information for clinically actionable decisions for the screening, early diagnosis, intervention, and prevention of cervical cancer in non-16/18 hrHPV-positive women.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".