Microbial composition analysis of<i>Clostridium difficile</i>infections in an ulcerative colitis patient treated with multiple fecal microbiota transplantations
Bibliographic record
Abstract
Fecal microbiota transplantation (FMT) is a promising therapy for Clostridium difficile infection (CDI). However, questions remain regarding efficacy and safety in inflammatory bowel disease (IBD) patients, as well as longitudinal stability of donor stool composition. This report describes an IBD patient with two CDIs 18 months apart, each successfully treated with FMT with no IBD flares or complications. Microbiome composition analysis of patient samples during each infection revealed low-diversity microbiota patterns similar to those previously described in non-IBD patients with CDI and active IBD alone. Samples taken after each transplant demonstrated quick remodeling towards the donor's sample composition coinciding with symptom resolution. Of note, samples taken from the same donor 18 months apart reflected marked differences in microbiota abundances, suggesting that the use of single donors in FMT programs offers little benefit in ensuring predictability of donor stool composition over time. This report describes similar microbial composition patterns during CDI in IBD patients to those described previously in non-IBD patients, and supports FMT as safe and effective treatment for recurring CDI in this patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".