Efficacy, Safety, and Concerns on Microbiota Modulation, Antibiotics, Probiotics, and Fecal Microbial Transplant for Inflammatory Bowel Disease and Other Gastrointestinal Conditions: Results from an International Survey
Bibliographic record
Abstract
The gut microbiota play a pivotal role in human health. Dysbiosis, alterations in microbiota composition and function, is associated with gastrointestinal disorders, including inflammatory bowel disease (IBD). This international survey aimed to assess physicians' experiences, perceptions, and practices related to microbiome modulation for gastrointestinal conditions, with a focus on IBD. Results from 142 healthcare professionals, predominantly gastroenterologists, confirmed a consensus on the relevance of the gut microbiota in IBD pathogenesis. However, the utilization of microbial composition analysis and probiotics in clinical practice was limited, primarily due to the lack of standardized guidelines and supporting evidence. Physicians held conflicting views on antibiotics, recognizing their potential for inducing remission but also causing flares in IBD. Respondents also had varying opinions on the efficacy of fecal microbiota transplantation (FMT) for different gastrointestinal conditions, with higher confidence in FMT effectiveness for irritable bowel syndrome with diarrhea, pouchitis, and ulcerative colitis. Concerns on FMT included uncertainty about effect duration, administration intervals, and conflicting evidence. Donor selection was believed to be a crucial factor in FMT outcomes. This survey highlights the need for further research and evidence-based guidelines to optimize the use of microbiome-based therapies in clinical practice. As our understanding of the gut microbiome continues to evolve, these insights will contribute to more informed and personalized approaches to managing gastrointestinal disorders.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".