Investigating fecal microbial transplant as a novel therapy in dogs with inflammatory bowel disease: A preliminary study
Bibliographic record
Abstract
BACKGROUND: There are limited studies investigating the use of fecal microbial transplant (FMT) in dogs with inflammatory bowel disease (IBD). The aim of this preliminary study was to assess the feasibility of adding FMT to standard therapy (corticosteroids and a hypoallergenic diet) for dogs with IBD and to and to describe the changes in measured outcomes after 30 days of treatment. METHODS: Thirteen client-owned dogs with IBD were enrolled in this double blinded, randomized clinical trial. All dogs received corticosteroid therapy and a hypoallergenic diet; dogs were randomized to receive either placebo or FMT. Measured outcomes included the canine chronic enteropathy clinical activity index (CCECAI) at 1 week and 1 month after enrolment. Fecal microbiota were analyzed after extracting DNA from fecal samples and profiling using 16S amplicon sequencing. Dogs in the placebo group not responding to treatment after 1 month were offered FMT. RESULTS: The CCECAI significantly decreased over time in both groups (p = 0.001). There were no significant differences between the CCECAI of the placebo and FMT group at each time point (F test from ANOVA, p = 0.40). No adverse effects were reported in the 30 days following FMT. CONCLUSIONS: The addition of FMT to standard therapy for IBD was feasible. No significant differences were observed in the CCECAI between groups at each time point. Large scale clinical trials can be performed using these methods to evaluate the longer term effect of FMT on clinical signs, microbial diversity, and other outcomes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".