Bibliographic record
Abstract
The dysbiosis theory of inflammatory bowel disease (IBD) posits that there is an alteration in the gut microbiome as an important underpinning of disease etiology. It stands to reason then, that administering agents that could impact on the balance of microbes on the gut could be impactful on the course of IBD. Herein is a review of the controlled trials undertaken to assess the use of antibiotics that would kill or suppress potentially injurious microbes, probiotics that would overpopulate the gut with potentially beneficial microbes or prebiotics that provide a metabolic substrate that enhances the growth of potentially beneficial microbes. With regard to antibiotics, the best data are for the use of nitroimadoles postoperatively in Crohn's disease (CD) to prevent disease recurrence. Otherwise, the data are limited with the regard to any lasting benefit of antibiotics sustaining remission in either CD or ulcerative colitis (UC). A recent meta-analysis concluded that antibiotics are superior to placebo at inducing remission in CD or UC, although the meta-analysis grouped a variety of antibiotics with different spectra of activity. Despite the absence of robust clinical trial data, antibiotics are widely used to treat perineal fistulizing CD and acute and chronic pouchitis. Probiotics have not been shown to have a beneficial role in CD. However, Escherichia coli Nissle 1917 has comparable effects to low doses of mesalamine in maintaining remission in UC. VSL#3, a combination of 8 microbes, has been shown to have an effect in inducing remission in UC and preventing pouchitis. Prebiotics have yet to be shown to have an effect in any form of IBD, but to date controlled trials have been small. The use of antibiotics should be balanced against the risks they pose. Even probiotics may pose some risk and should not be assumed to be innocuous especially when ingested by persons with a compromised epithelial barrier. Prebiotics may not be harmful but may cause gastrointestinal side effects. Finally, the timing of ingestion of antibiotics and other dietary factors that may function as prebiotics, especially in early childhood, may be critical in shaping the gut microbiome and ultimately predisposing to or preventing IBD. Finding ways to impact on the gut microbiome to alter the course of IBD makes good sense, but should be undertaken in the setting of rigorously performed controlled trials to ensure that the interventions are truly effective and well tolerated.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".