The Effect of Fiber Supplementation on Irritable Bowel Syndrome: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Fiber has been used for many years to treat irritable bowel syndrome (IBS). This approach had fallen out of favor until a recent resurgence, which was based on new randomized controlled trial (RCT) data that suggested it might be effective. We have previously conducted a systematic review of fiber in IBS, but new RCT data for fiber therapy necessitate a new analysis; thus, we have conducted a systematic review of this intervention. METHODS: MEDLINE, EMBASE, and the Cochrane Controlled Trials Register were searched up to December 2013. Trials recruiting adults with IBS, which compared fiber supplements with placebo, control therapy, or "usual management", were eligible. Dichotomous symptom data were pooled to obtain a relative risk (RR) of remaining symptomatic after therapy as well as number needed to treat (NNT) with a 95% confidence interval (CI). RESULTS: We identified 14 RCTs involving 906 patients that had evaluated fiber in IBS. There was a significant benefit of fiber in IBS (RR=0.86; 95% CI 0.80-0.94 with an NNT=10; 95% CI=6-33). There was no significant heterogeneity between results (I(2)=0%, Cochran Q=13.85 (d.f.=14), P=0.46). The benefit was only seen in RCTs on soluble fiber (RR=0.83; 95% CI 0.73-0.94 with an NNT=7; 95% CI 4-25) with no effect seen with bran (RR=0.90; 95% CI 0.79-1.03). CONCLUSIONS: Soluble fiber is effective in treating IBS. Bran did not appear to be of benefit, although we did not uncover any evidence of harm from this intervention, as others have speculated from uncontrolled data.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".