Alginate therapy is effective treatment for GERD symptoms: a systematic review and meta-analysis
Bibliographic record
Abstract
In patients with gastroesophageal reflux disease (GERD) and erosive esophagitis, treatment with proton pump inhibitors (PPIs) is highly effective. However, in some patients, especially those with nonerosive reflux disease or atypical GERD symptoms, acid-suppressive therapy with PPIs is not as successful. Alginates are medications that work through an alternative mechanism by displacing the postprandial gastric acid pocket. This study performed a systematic review and meta-analysis to examine the benefit of alginate-containing compounds in the treatment of patients with symptoms of GERD. PubMed/MEDLINE, Embase, and the Cochrane library electronic databases were searched through October 2015 for randomized controlled trials comparing alginate-containing compounds to placebo, antacids, histamine-2 receptor antagonists (H2RAs), or PPIs for the treatment of GERD symptoms. Additional studies were identified through a bibliography review. Non-English studies and those with pediatric patients were excluded. Meta-analyses were performed using random-effect models to calculate odds ratios (OR). Heterogeneity between studies was estimated using the I2 statistic. Analyses were stratified by type of comparator. The search strategy yielded 665 studies and 15 (2.3%) met inclusion criteria. Fourteen were included in the meta-analysis (N = 2095 subjects). Alginate-based therapies increased the odds of resolution of GERD symptoms when compared to placebo or antacids (OR: 4.42; 95% CI 2.45-7.97) with a moderate degree of heterogeneity between studies (I2 = 71%, P = .001). Compared to PPIs or H2RAs, alginates appear less effective but the pooled estimate was not statistically significant (OR: 0.58; 95% CI 0.27-1.22). Alginates are more effective than placebo or antacids for treating GERD symptoms.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".