Clinical Management of the Microbiome in Irritable Bowel Syndrome
Bibliographic record
Abstract
BACKGROUND: A growing body of evidence suggests that dysbiosis contributes to the onset and symptomatology of irritable bowel syndrome (IBS) and other functional bowel disorders. Changes to the gastrointestinal microbiome may contribute to the underlying pathophysiology of IBS. METHODS: The present review summarizes the potential effects of microbiome changes on GI transit, intestinal barrier function, immune dysregulation and inflammation, gut-brain interactions and neuropsychiatric function. RESULTS: A multimodal approach to IBS management is recommended in accordance with current Canadian guidelines. Pharmacologic treatments are advised to target the presumed underlying pathophysiological mechanism, such as dysregulation of GI transit, peristalsis, intestinal barrier function and pain signalling. The management plan for IBS may also include treatments directed at dysbiosis, including dietary modification and use of probiotics, which may promote the growth of beneficial bacteria, affect intestinal gas production and modulate the immune response; and the administration of periodic short courses of a nonsystemic antibiotic such as rifaximin, which may re-establish microbiota diversity and improve IBS symptoms. CONCLUSION: Dysregulated host-microbiome interactions are complex and the use of microbiome-directed therapies will necessarily be empiric in individual patients. A management algorithm comprising microbiome- and nonmicrobiome-directed therapies is proposed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".