Meta-analysis about correlation between the human Papillomavirus infection and the incidence of cervical intraepithelial Neoplasia
Bibliographic record
Abstract
OBJECTIVE: To determine the correlations between human papillomavirus infection and the incidence of cervical intraepithelial neoplasia. METHODS: This study was conducted in the Affiliated Hospital of Zunyi Medical University, Zunyi, China in January 2024. The systematic review and meta-analysis comprised literature search on PubMed, Medline, Embase, Cochrane Library, Web of Science, China Biomedical and Wanfang databases for studies published from January 2010 to December 2020 related to human papillomavirus and cervical intraepithelial neoplasia. The quality of the studies was evaluated using the Newcastle Ottawa Scale, and meta-analysis was done using RevMan 5.3. RESULTS: Of the 854 studies identified, 10(1.2%) were included; 7(70%) in English and 3(30%) in Chinese. There was a total of 193,000 patients; 94,298(49%) in the observation group and 98,702(51%) in the control group. Human papillomavirus infection was closely correlated with cervical intraepithelial neoplasia-1, cervical intraepithelial neoplasia-2 and cervical intraepithelial neoplasia-3 in women, with odds ratios of 3.94 (95% confidence interval: 3.53-4.40), 1.03 (95% confidence interval: 1.01-1.06) and 1.13 (95% confidence interval: 1.10-1.16), respectively. Both human papillomavirus single infection and reinfection in cervical intraepithelial neoplasia patients were significantly higher than in normal women, with odds ratios of 0.50 (95% confidence interval: 0.41-0.61) and 0.43 (95% confidence interval: 0.35-0.53), respectively. CONCLUSIONS: The incidence of cervical intraepithelial neoplasia was found to be highly associated with human papilomavirus infection, and the infection increased the risk of cervical diseases..
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.061 |
| Bibliometrics | 0.007 | 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) | 0.003 | 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".