Repeated Fecal Microbial Transplantations and Antibiotic Pre-Treatment Are Linked to Improved Clinical Response and Remission in Inflammatory Bowel Disease: A Systematic Review and Pooled Proportion Meta-Analysis
Bibliographic record
Abstract
The response of patients with inflammatory bowel disease (IBD) to fecal microbial transplantation (FMT) has been inconsistent possibly due to variable engraftment of donor microbiota. This failure to engraft has resulted in the use of several different strategies to attempt optimization of the recipient microbiota following FMT. The purpose of our study was to evaluate the effects of two distinct microbial strategies—antibiotic pre-treatment and repeated FMT dosing—on IBD outcomes. A systematic literature review was designed and implemented in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A medical librarian conducted comprehensive searches in MEDLINE, Embase, Scopus, Web of Science Core Collection, and Cochrane Library on 25 November 2019 and updated on 29 January 2021. Primary outcomes of interest included comparing relapse and remission rates in patients with IBD for a single FMT dose, repeated FMT dosages, and antibiotic pre-treatment groups. Twenty-eight articles (six randomized trials, 20 cohort trials, two case series) containing 976 patients were identified. Meta-analysis revealed that both repeated FMT and antibiotic pre-treatment strategies demonstrated improvements in pooled response and remission rates. These clinical improvements were associated with increases in fecal microbiota richness and α-diversity, as well as the enrichment of several short-chain fatty acid (SCFA)-producing anaerobes including Bifidobacterium, Roseburia, Lachnospiraceae, Prevotella, Ruminococcus, and Clostridium related species.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.049 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".