Fecal microbial transplant for the treatment of pediatric inflammatory bowel disease
Bibliographic record
Abstract
The role of fecal microbial transplant (FMT) in the treatment of pediatric gastrointestinal disease has become increasingly popular among pediatric practitioners, patients, and parents.The success of FMT for the treatment of recurrent Clostridium difficile infection (RCDI) has bolstered interest in its potential application to other disease states, such as inflammatory bowel disease (IBD).FMT has particular interest in pediatrics, given the concerns of patients and parents about rates of adverse events with existing therapeutic options, and the greater cumulative medication burden associated with childhoodonset disease.Published literature on the use of FMT in pediatrics is sparse.Only 45 pediatric patients treated for RCDI have been reported, and only 27 pediatric patients with pediatric IBD.The pediatric microbiome may uniquely respond to microbial-based therapies.This review will provide a comprehensive overview of fecal microbial transplant and its potential role in the treatment of pediatric inflammatory bowel disease.We will discuss the microbiome in pediatric inflammatory bowel disease, existing adult and pediatric literature on the use of FMT in IBD treatment, and pediatric FMT trials that are currently recruiting patients.This review will also discuss features of the microbiome that may be associated with host response in fecal transplant, and potential challenges and opportunities for the future of FMT in pediatric IBD treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".