Maternal alcohol consumption and the risk of miscarriage in the first and second trimesters: A systematic review and dose–response meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: According to a precautionary principle, it is recommended that pregnant women and women trying to conceive abstain from alcohol consumption. In this dose-response meta-analysis, we aimed to examine the association between alcohol consumption and binge drinking and the risk of miscarriage in the first and second trimesters. MATERIAL AND METHODS: The literature search was conducted in MEDLINE, Embase and the Cochrane Library in May 2022, without any language, geographic or time limitations. Cohort or case-control studies reporting dose-specific effects adjusting for maternal age and using separate risk assessments for first- and second-trimester miscarriages were included. Study quality was assessed using the Newcastle-Ottawa Scale. This study is registered with PROSPERO, registration number CRD42020221070. RESULTS: A total of 2124 articles were identified. Five articles met the inclusion criteria. Adjusted data from 153 619 women were included in the first-trimester analysis and data from 458 154 women in the second-trimester analysis. In the first and second trimesters, the risk of miscarriage increased by 7% (odds ratio [OR] 1.07, 95% confidence interval [CI] 0.96-1.20) and 3% (OR 1.03, 95% CI 0.99-1.08) for each additional drink per week, respectively, but not to a statistically significant degree. One article regarding binge drinking and the risk of miscarriage was found, which revealed no association between the variables in either the first or second trimester (OR 0.84 [95% CI 0.62-1.14] and OR 1.04 [95% CI 0.78-1.38]). CONCLUSIONS: This meta-analysis revealed no dose-dependent association between miscarriage risk and alcohol consumption, but further focused research is recommended. The research gap regarding miscarriage and binge drinking needs further investigation.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.004 | 0.005 |
| 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".