Self-stool banking as a source for fecal microbiota transplantation: A pilot study
Bibliographic record
Abstract
Background: Fecal microbiota transplantation is a promising therapeutic alternative for refractory Clostridium difficile infection. Self-stool donation can overcome challenges with donor screening and eliminate risks of blood borne pathogen exposure. We assessed the feasibility of a fecal banking protocol. A secondary objective was to identify perceptions around fecal banking. Methods: Admitted medicine patients were screened over 15 months. Patients with gastrointestinal comorbidities or factors affecting intestinal microbiota were excluded. Participants completed a survey and could opt to bank a sample. Processing occurred during defined lab hours. Feasibility was assessed on process and resource indicators. Success was defined as 50% consent rate. Results: A total of 4,675 patients were screened; 60% were excluded, primarily because of antibiotic exposure (1,343, 48%). A total of 537 patients were surveyed, of whom 73% consented to fecal banking. The primary reason for declining was that fecal banking was considered ‘too gross’ (34%). Of 72 samples provided, 27 were successfully banked. Lack of a bowel movement was the primary reason for not banking (54%), and inadequate quantity was the top reason for rejecting a collected sample (63%). Average processing time was 58 minutes (range 22–640 min). The majority of participants reported a preference for using their own stool (82%), and 87.5% (n=64) were willing to bank again. Conclusion: A self-donor fecal banking protocol is feasible. Scalability of this process is addressed through dedicated resources for collecting and processing samples from larger cohorts. Formal evaluation of the efficacy and microbiome integrity of samples is required.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".