Improvements in Gut Microbiome Composition and Clinical Symptoms Following Familial Fecal Microbiota Transplantation in a Nineteen-Year-Old Adolescent With Severe Autism
Bibliographic record
Abstract
This case report describes a novel therapy for patients with severe autism spectrum disorder (ASD) that is worth further investigation. A 19-year-old male adolescent with ASD, who was not responding to standard treatment received fecal microbiota transplant (FMT) using donor material from his typically developing female sibling. The patient's ASD symptoms were assessed by assessors who were blind to the patient's past ASD symptomatology. Assessors used the Childhood Autism Rating Scale (CARS), an observation-based rating scale to assess developmental delay in children with autism (range of CARS scores is 15 - 60; a score > 28 is indicative of autism; higher score is positively correlated with degree of severity), at baseline and again at six timepoints post-FMT. The patient experienced marked improvements in microbiome diversity and composition over the year and a half period that followed the FMT procedure. Additionally, the patient who was previously nonverbal said his first two words and experienced a reduction in aggression 1-month post-FMT. To the authors' knowledge, this is the first report to demonstrate the use of familial FMT in an adolescent patient with ASD. Given that ASD symptom improvements post-FMT tend to occur in younger patients, the authors hypothesize that the use of a familial donor may be an important factor that contributed to the improved outcomes experienced by this older child.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".