Alginate Therapy for Gastroesophageal Reflux in Pregnancy: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Alginate therapies are a promising option for managing gastroesophageal reflux (GERD), but most treatments are not recommended during pregnancy. This scoping review aimed to summarize the current outcomes research on the use of alginate therapy for treating GERD during pregnancy. DATA SOURCES: EMBASE, MEDLINE, CINAHL, and Web of Science were searched from database inception to November 1, 2024. Reference lists of included studies were also searched. REVIEW METHODS: We included randomized controlled trials (RCTs) and non-randomized studies evaluating alginate use in pregnant individuals with GERD. Screening and data extraction were performed by three reviewers in duplicate. Extracted data included study design, location, population, study group allocation, and clinical outcomes. Descriptive statistics were calculated using Microsoft Excel. RESULTS: Two RCTs and two prospective cohort studies met the inclusion criteria. Of the two RCTs identified, one evaluated an alginate formulation against a proton pump inhibitor (PPI), and the other against a magnesium-aluminum antacid. Cohort studies reported investigator- and patient-rated treatment success at 89.7% and 90.0%, respectively, with 92.2% of participants having symptom relief within 20 min. Similar improvements in heartburn intensity and frequency were found when comparing alginates and antacids. Few maternal adverse effects were reported, and no fetal or neonatal outcomes were related to treatment. Safety profiles of alginates were comparable to PPIs and antacids. CONCLUSION: Current literature on alginate therapy in pregnant women may suggest potential for symptom relief and favorable tolerability, but current evidence is limited by the few available prospective studies. LEVEL OF EVIDENCE: N/A.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".