Chronic hepatitis C virus infection is associated with increased risk of preterm birth: a meta‐analysis of observational studies
Bibliographic record
Abstract
Although several epidemiological studies reported that maternal chronic hepatitis C virus (HCV) infection had significantly increased risk of undergoing adverse obstetrical and perinatal outcomes, studies on the relationship between HCV infection and risk of preterm birth (PTB) have yielded inconclusive and inconsistent results. Therefore, we conducted a meta-analysis to investigate the association between HCV infection and PTB. The electronic database was searched until 1 September 2014. Relevant studies reporting the association between HCV infection and the risk of PTB were included for further evaluation. Statistical analysis was performed using revmen 5.3 and stata 10.0. Nine studies involving 4186698 participants and 5218 HCV infection cases were included. A significant association between HCV infection and PTB was observed (odds ratio = 1.62, 95% CI 1.48-1.76, P < 0.001, fixed-effects model). Stratification according to maternal smoking/alcohol abuse, maternal drug abuse or coinfected with HBV and/or HIV matched groups still demonstrated that women with HCV infection had a high risk for PTB. Findings from our meta-analysis suggested that maternal HCV infection was significantly associated with an increased risk of PTB. In the future, pathophysiological studies are warranted to ascertain the causality and explore the possible biological mechanisms involved.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.025 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".