Influence of Human Papillomavirus Infection on the Natural History of Cervical Intraepithelial Neoplasia 1: A Meta-Analysis
Bibliographic record
Abstract
Objective. To provide a scientific basis for the prevention and treatment of cervical intraepithelial neoplasia grade 1 (CIN1). This study evaluated the impact of human papillomavirus (HPV) infection on the natural history of CIN1. Methods. Electronic databases of Cochrane Library, EMBASE, PubMed, CNKI, CBM, and Wanfang were searched in April 2016. The eligibility criteria were documented by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We used the Newcastle-Ottawa scale (NOS) to assess study quality. Results. Thirty-eight studies out of 3,246 identified papers were eligible for inclusion. The risk of CIN1 progression (relative risk [RR]: 3.04; 95% confidence interval [CI]: 2.41–3.83; P<0.00001 ) and persistence (RR: 1.48; 95% CI: 1.17–1.87; P=0.001 ) was higher in the HPV-positive group than HPV-negative group. Specifically, the risk of CIN1 progression (RR: 13.91; 95% CI: 3.46–55.90; P=0.000 ) was higher among persistent high-risk HPV-positive patients and the ratio of CIN1 regression (RR: 0.65; 95% CI: 0.59–0.71; P<0.00001 ) was lower in the HPV-positive group than HPV-negative group. Conclusion. HPV infection resulted in an increased risk of CIN1 progression and decreased disease reversibility. Persistent high-risk HPV infection resulted in a further increased risk of CIN1 progression.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.076 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".