Association of hepatitis C virus infection with risk of gestational diabetes mellitus: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Aim Whether hepatitis C virus (HCV) infection is associated with the risk of gestational diabetes mellitus (GDM) remains controversial. This study aims to systematically evaluate the impact of HCV infection on GDM risk. Methods Seven electronic databases, including PubMed, Embase, Web of Science, Ovid, ScienceDirect, China National Knowledge Infrastructure, and SinoMed, were retrieved for all related studies by October of 2024. Studies on the relationship between HCV infection and GDM risk were included. The quality of included studies was assessed using the Newcastle‐Ottawa Scale. A random effects meta‐analysis was performed to estimate the overall odds ratio (OR) and 95% confidence intervals (CI) by Stata software. Results The analysis included 18 studies comprising 45 567 678 pregnancies. HCV infection was significantly associated with increased GDM risk (pooled OR = 1.21, 95% CI: 1.04–1.40), with substantial heterogeneity ( I 2 = 87.6%). Subgroup analyses demonstrated significant associations in European populations (OR = 1.66, 95% CI: 1.13–2.43; I 2 = 40.4%), prospective cohort studies (OR = 1.72, 95% CI: 1.33–2.22; I 2 = 0.0%), human immunodeficiency virus‐positive populations (OR = 2.04, 95% CI: 1.04–3.99; I 2 = 46.2%), and general populations (OR = 1.16, 95% CI: 1.00–1.34; I 2 = 88.7%). There was a positive but not statistically significant association for the North America population and retrospective studies ( p < 0.05), with considerable heterogeneity. The overall results were robust, indicated by sensitivity analysis, and no obvious publication bias was observed according to Begg's test. Conclusions HCV infection is positively associated with increased GDM risk. Further studies with diverse representative populations and more rigorous prospective designs are warranted to establish more comprehensive conclusions.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".