Probiotics in the Treatment of Inflammatory Bowel Disease
Bibliographic record
Abstract
The demonstration that immune and epithelial cells can discriminate between different microbial species has extended our understanding of the actions of probiotics beyond simple antimicrobial concepts. Several probiotic mechanisms of action, relative to inflammatory bowel disease, have been elucidated: (1) competitive exclusion, whereby probiotics compete with microbial pathogens; (2) immunomodulation and/or stimulation of an immune response; (3) antimicrobial activity and suppression of pathogen growth; (4) enhancement of barrier activity; and (5) induction of T cell apoptosis. The unraveling of these mechanisms of action has led to new support for the use of probiotics in the management of clinical inflammatory bowel disease. While level 1 evidence now supports the therapeutic use of some probiotics in the maintenance treatment of pouchitis, only level 2 and 3 evidence are currently available in support of the use of probiotics in the treatment of ulcerative colitis and Crohn's disease. Nevertheless, one significant and consistent finding has emerged over the course of research in the past year: not all probiotic bacteria have similar therapeutic effects. Rigorously designed, controlled clinical trials, to investigate the unresolved issues related to efficacy, dose, duration of use, single or multistrain formulation, and the concomitant use of prebiotics, synbiotics or antibiotics, are vital.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".