Cumulative risk of cervical intraepithelial neoplasia for women with normal cytology but positive for human papillomavirus: Systematic review and meta‐analysis
Bibliographic record
Abstract
Most women positive for human papillomavirus (HPV) are cytology normal. The optimal screen-management of these women is unclear given their risk of developing precancer. We performed a systematic review and meta-analysis of progression rates to precancer and cancer for HPV-positive, cytology normal women. We searched MEDLINE, EMBASE and Scopus for prospective studies measuring the cumulative incidence of precancer and cervical cancer in HPV-positive, cytology/histology normal women. Record screening was performed independently by two reviewers. We modeled the cumulative incidence over time using a multilevel random-effects meta-regression model. We used the model to predict HPV type-specific risks of precancer and cancer over follow-up. Data from 162 unique records were used in our analysis. The average incidence rate of cervical intraepithelial neoplasia grade 3 or cancer (CIN3+) in high-risk HPV positive but cytology/histology normal women was 1.0 per 100 women-years (95% CI: 1.0-1.1). This corresponds to an average cumulative risk at 1, 3 and 5 years of 2.1% (95% prediction interval 0.0-9.5), 4.3% (95% prediction interval 0.0-11.5) and 6.4% (95% prediction interval 0.0-13.5). HPV type was a strong predictor of the risk of oncogenic progression. There was substantial heterogeneity in the background precancer risk across studies (P-value < .0001). Our HPV type-specific progression risk estimates can help inform risk-based cervical cancer screening guidelines for HPV-positive women. However, precancer and cervical cancer risks are highly variable and may not be generalizable between populations.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".