Manipulation of Intestinal Microbial Flora for Therapeutic Benefit in Inflammatory Bowel Diseases: Review of Clinical Trials of Probiotics, Prebiotics and Synbiotics
Bibliographic record
Abstract
Pathogenesis of Inflammatory Bowel Diseases(Ulcerative Colitis, Crohn's disease and Pouchitis) includes an abnormal immunological response to disturbed intestinal microflora. Therapeutic strategies are designed to intervene in these abnormal host microbial communications. A novel approach in the last decade has been to use other bacteria or selective foods to induce beneficial bacteria to normalize inflammation. In this review we discuss rationale for such use and describe 46 clinical trials gleaned from the literature. Reports are divided into type, indications, and agents used. The search revealed 15 nonrandomized and 31 randomized trials. Of the latter 23 were double-blind and 8 were open-label randomized controlled. In 32 of the total, different probiotics were used, while 10 and 4 used different prebiotics or synbiotics respectively. In 14 nonrandomized trials, outcome was successful. In the randomized controlled trials 12 of 16 ulcerative colitis but only 2 of Crohn's disease trials of biotic therapy were successful. No superiority of any probiotic was clearly evident, but a multi-agent mixture, VSL3# may be better suited in ulcerative colitis and pouchitis while the probiotic Lactobacillus rhamnosus GG appears less useful in inflammatory bowel disease, especially Crohn's disease. Further studies with uniform stringent criteria are needed to provide proof of this therapy in inflammatory bowel disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".