Understanding Why Probiotic Therapies Can be Effective in Treating IBD
Bibliographic record
Abstract
Probiotics, for the treatment of inflammatory bowel disease, are a group of specific nonpathogenic bacteria that are functionally and genetically defined by their ability to reduce inflammation in the intestine. Although probiotics also seem to have broad beneficial effects in humans, both as a food and as a therapeutic agent, there are specific identified mechanisms in some, but not all, of these bacteria that are important relative to the pathogenesis of inflammatory bowel disease. Recently, studies relative to the mechanism of action of probiotics have identified that these organisms can have a direct effect on epithelial cell function and intestinal health, including enhancing epithelial barrier function, modulating epithelial cytokine secretion into an anti-inflammatory dominant profile, altering mucus production, changing bacterial luminal flora, modifying the innate and systemic immune system, and inducing regulatory T-cell effects. For probiotics to have a therapeutic role in the management of clinical inflammatory bowel disease, their therapeutic mechanism of action must be aligned with the pathogenic mechanism of action of the disease. In this regard, the role of probiotics for the clinical treatment of inflammatory bowel disease is emerging as the mechanisms and pathogenesis are being unraveled. It remains clear that probiotics are able to reduce gastrointestinal inflammation by exerting positive effects on epithelial cell and mucosal immune dysfunction.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".