Cognitive behavioural therapy-based interventions for gastroduodenal disorders of gut-brain interaction: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Cognitive behavioural therapy (CBT) is increasingly used to manage Disorders of Gut-Brain Interaction (DGBIs). This systematic review aimed to review the evidence for the effectiveness of CBT-based interventions for patients with gastroduodenal DGBIs. METHODS: Medline, Embase, PubMed, Cochrane Central, and Scopus were searched in July 2022. Studies were included if they investigated the effects of a CBT-based intervention on gastrointestinal symptoms and/or psychological outcomes pre- and post-intervention in patients with gastroduodenal DGBIs. Case studies, studies not in English, and studies with patients under 18 years were excluded. Results were synthesised narratively, and standardised effect sizes were calculated where possible. RESULTS: Nine studies (seven RCTs and two pre/post studies) were identified, with data reported in 10 articles (total N = 602). The studies investigated patients with functional dyspepsia (n = 7), rumination syndrome (n = 1), and supragastric belching (n = 1). The studies had heterogeneous interventions, methodologies, and outcomes, precluding meta-analysis, as well as a moderate-high risk of bias and high drop-outs rates. Findings demonstrated decreased gastrointestinal symptoms and improved anxiety, depression, and quality of life, from pre- to post-intervention, with medium to large effect sizes for symptoms and small to large effect sizes for psychological outcomes. Efficacy was maintained at follow-up, up to one year later. CONCLUSIONS: This review suggests promising evidence that CBT effectively improves gastrointestinal symptoms and psychological outcomes in patients with gastroduodenal DGBIs. However, heterogeneity, risk of bias, and lack of statistical reporting were noted, indicating the need for more robust research and standardisation.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".