The detrimental effect of autoimmune hepatitis in pregnancy: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Autoimmune hepatitis (AIH), a chronic inflammatory liver disease, poses unique challenges during pregnancy. In AIH, the immune response triggers inflammation, potentially leading to cirrhosis if left untreated. The study aims to provide comprehensive insights into the relationship between AIH and pregnancy, guiding clinicians in optimal maternal management. Methods: Our meta-analysis includes data searched up to 1 January 2024. Eligibility criteria encompassed studies focusing on pregnant individuals with AIH, excluding case reports, letters, reviews, and incomplete papers. Data extraction, quality assessment using Cochrane risk of bias or Newcastle–Ottawa Scale, and quantitative analysis through Comprehensive Meta-Analysis were performed. Results: The literature search included 15 studies, involving 38 679 pregnant women with AIH across 10 countries. Maternal outcomes, such as disease flare [event rate ( E ) = 0.228, 95% CI: 0.13–0.36; P = 0.000] and maternal deaths, indicated significant associations. The incidence of gestational diabetes was higher in AIH patients ( E = 0.091, 95% CI: 0.043–0.184; P = 0.001), showing notable heterogeneity. Fetal outcomes demonstrated significant associations with preterm births ( E = 0.190, 95% CI: 0.131–0.268; P = 0.000), fetal loss ( E = 0.142, 95% CI: 0.082–0.236; P = 0.000), and congenital anomalies ( E = 0.041, 95% CI: 0.026–0.065; P = 0.000). Conclusion: This systematic review contributes a comprehensive overview of the complex interplay between AIH and pregnancy, providing evidence for heightened risks and implications on maternal and fetal outcomes. Clinicians can leverage these findings for informed decision-making, while the study enriches the broader field of autoimmune and reproductive health.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".