A systematic review of the efficacy and safety of turmeric in the treatment of digestive disorders
Bibliographic record
Abstract
Turmeric has been gaining popularity as a treatment option for digestive disorders, although a rigorous synthesis of efficacy has not been conducted. This study aimed to summarize the evidence for the efficacy and safety of turmeric in the treatment of digestive disorders, including inflammatory bowel diseases (IBD), irritable bowel syndrome (IBS), dyspepsia, gastroesophageal reflux disease, and peptic ulcers. Literature searches were conducted in Medline, EMBASE, AMED, the Cochrane Central Register of Control Trials, and Dissertation Abstracts from inception to November 15, 2021. Dual independent screening of citations and full texts was conducted and studies meeting inclusion criteria were retained: randomized controlled trials (RCT) and comparative observational studies evaluating turmeric use in people of any age with one of the digestive disorders of interest. Extraction of relevant data and risk of bias assessments were performed by two reviewers independently. Meta-analysis was not conducted due to high heterogeneity. From 1136 citations screened, 26 eligible studies were retained. Most studies were assessed to have a high risk of bias, and many had methodological limitations. Descriptive summaries suggest that turmeric is safe, with possible efficacy in patients with IBD or IBS, but its effects were inconsistent for other conditions. The efficacy of turmeric in digestive disorders remains unclear due to the high risk of bias and methodological limitations of the included studies. Future studies should be designed to include larger sample sizes, use rigorous statistical methods, employ core outcome sets, and adhere to reporting guidance for RCTs of herbal interventions to facilitate more meaningful comparisons and robust conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".