Evaluation of Alternative Treatment Strategies for Bile Acid Malabsorption in Inflammatory Bowel Disease Patients: A Network Meta-Analysis
Bibliographic record
Abstract
Background: Bile acid malabsorption (BAM) is characterized by chronic watery diarrhea resulting from excessive bile acids in the feces. BAM is often an overlooked cause of chronic diarrhea, with its prevalence not being sufficiently researched. This review aimed to assess existing literature that explores diverse treatment strategies, to review the published studies that examine the various therapies for BAM patients, emphasizing their influence on clinical results. Methods: We conducted a comprehensive review of various databases, including PubMed, Scopus, Web of Science, Cochrane Database, and EMBASE. Our criteria for inclusion focused on randomized controlled studies (RCTs) that evaluated the effectiveness of different treatment options for patients with BAM. To rank the treatments, we adopted the frequentist approach through the "netrank" function of the network meta-analysis (NMA). Moreover, we utilized the "netsplit" function in the NMA to separate direct and indirect evidence. Our analysis was carried out using RStudio version 1.4.1717 (2009 - 2021 RStudio, Inc.), and we used the "netmeta" and "meta" packages for NMA. Results: We found seven relevant articles involving 213 participants, the average age being approximately 50 years, including 53 males and 92 females. Of the drugs examined, tropifexor was proved to be the most effective in raising the fibroblast growth factor 19 (FGF19) levels and reducing the 7 alpha-hydroxy-4-cholesten-3-one (C4) levels, compared to the placebo (mean difference (MD) = 335.30, 95% confidence interval (CI) (334.86, 335.74), MD = -24.60, 95% CI (-25.37, -23.83); respectively). Compared to colesevelam and the placebo, liraglutide was more efficient in decreasing fecal bile acid concentration (liraglutide; MD = -19, 95% CI (-37.61, -0.39)). Conclusions: Tropifexor has been identified as the most successful medication in mitigating BAM symptoms. To ensure more accurate results, there is a need for randomized controlled clinical trials that involve a larger participant pool.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.052 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".