A meta-analysis of the relationship between vaginal microecology, human papillomavirus infection and cervical intraepithelial neoplasia
Bibliographic record
Abstract
Abstract Microecology is an emerging discipline in recent years. The female reproductive tract is an important microecological region, and its microecological environment can directly affect women’s cervical health. This meta-analysis aimed to analyze the effects of vaginal microecology on Human papillomavirus (HPV) infection and cervical intraepithelial neoplasia (CIN). PubMed and Web of Science were systematically searched for eligible publications from January 2000 to December 2017. Articles were selected on the basis of specific inclusion and exclusion criteria. The design and quality of all studies were evaluated using the Newcastle-Ottawa Scale (NOS). Odds ratios (ORs) with a 95% confidence interval (95% CI) were calculated. Thirteen eligible studies were selected to evaluate the association of vaginal microecology with HPV infection and CIN. The factors related to HPV infection were bacterial vaginosis (BV) (OR 2.57, 95% CI 1.78–3.71, P<0.05), Candida albicans (VVC) (OR 0.63, 95% CI 0.49–0.82, P < 0.05), Chlamydia trachomatis (CT) (OR 3.16, 95% CI 2.55–3.90, P < 0.05), and Ureaplasma urealyticum (UU) (OR 1.35, 95% CI 1.20–1.51, P < 0.05). BV was also related to CIN (OR 1.56, 95% CI 1.21–2.00, P < 0.05). This meta-analysis of available literature suggested an intimate association of vaginal microecology and HPV infection with CIN. BV, CT and UU were associated to increased HPV infection, VVC was associated to decreased HPV infection, Lactobacillus is not associated to increased HPV infection, BV was associated to increased CIN development risk. Further large-scale studies are needed to confirm our findings.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.055 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".