Global prevalence and risk of gastroesophageal reflux disease symptoms in pregnancy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Gastroesophageal reflux disease (GERD), manifested by heartburn and regurgitation, is a common pregnancy complaint that significantly impairs quality of life. Reported prevalence varies widely, with differences observed across pregnancy trimesters. This first systematic review and meta-analysis synthesizing global data aimed to estimate the pooled prevalence and risk of GERD symptoms during pregnancy, including trimester-specific trends. METHODOLOGY: Following PRISMA 2020 guidelines, a comprehensive literature search was conducted across MEDLINE/PubMed, EMBASE, Cochrane Library, and Google Scholar from 1 January 1975 to 10 July 2025. The protocol was registered in PROSPERO (CRD420251102794). Eligible studies included observational studies reporting the prevalence of GERD symptoms in pregnant populations. Two independent reviewers screened articles, extracted data, and assessed methodological quality using the Newcastle-Ottawa Scale. A random-effects meta-analysis model was applied. RESULTS: A total of 21 studies involving 9497 pregnant participants were included. The pooled prevalence of GERD symptoms during pregnancy was 40.96% (95% CI: 27.63-54.28). Subgroup analysis by trimester revealed a progressive increase in GERD prevalence, from 26.12% in the first trimester to 33.40% in the second, peaking at 55.93% in the third, reflecting the growing physiological burden. In studies that included non-pregnant control groups, pregnant women had a significantly higher risk of experiencing GERD symptoms compared to non-pregnant controls (OR = 8.37; 95% CI: 2.93-23.89). Subgroup analysis showed that the pooled prevalence of heartburn was 43.81% (95% CI: 23.74-63.87), while regurgitation affected 37.94% (95% CI: 26.23-49.65) of pregnant women. CONCLUSION: GERD symptoms are highly prevalent during pregnancy, especially in the third trimester. Early identification and management may improve maternal comfort and quality of life.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.004 | 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".