Fecal microbiota transplant in severe and severe-complicated<i>Clostridium difficile</i>: A promising treatment approach
Bibliographic record
Abstract
Severe and severe-complicated Clostridium difficile infection (CDI) is associated with high morbidity and mortality. Colectomy is standard of care; however, post-surgical mortality rates approach 50%. Case reports suggest fecal microbiota transplant (FMT) is a promising treatment of severe and severe-complicated disease but there is a paucity of data. Here, we present a single center experience with a novel sequential FMT protocol for patients refractory to maximal medical therapy. This approach consists of at least one FMT delivered via colonoscopy with criteria for repeat FMT and continued vancomycin therapy based on clinical response and pseudomembranes. Our cohort included 57 consecutive inpatients diagnosed with severe or severe-complicated CDI and treated with FMT. Overall, 91% (52/57) experienced clinical cure at 1 month with a 100% cure rate among severe CDI (n = 19) patients and an 87% cure rate for severe-complicated CDI (n = 33) patients. For the cohort, the survival rate was 94.7% at 1 month and 78.6% at 3 months. There were no serious adverse events related to FMT including no procedure-related complications or perforation. There was no difference in outcome between fresh or frozen fecal material. Sequential FMT for inpatients with severe or severe-complicated CDI is promising and may be preferred over colectomy in certain patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".