Challenges establishing a multi-purpose fecal microbiota transplantation stool donor program in Toronto, Canada
Bibliographic record
Abstract
Background: The success of fecal microbiota transplantation (FMT) programs depends on maintaining suitable stool donors. We describe challenges recruiting and retaining universal donors in the first 2 years of an FMT clinical and research program in Toronto and identify opportunities for improvement. Methods: A four-stage screening process is used to identify suitable FMT donors in the Microbiota Therapeutics Outcomes Program. Donor screening follows Health Canada recommendations and excludes persons with history or risk for diseases associated with dysbiosis. Donors are rescreened microbiologically approximately every 1–3 months and answer ongoing health, exposure, and dietary questionnaires. Results: In the first 2 years of our program, 5 of 322 (1.6%) prospective stool donors passed initial screening, and only 2 (0.6%) were retained. Most prospective donors were excluded on telephone screening, at which point high BMI, medication use, and family history of relevant illness were common exclusions. No candidate was excluded because of a concerning physical examination. Microbiologic reasons for donor exclusion included carriage of Blastocystis hominis ( n = 2), Helicobacter pylori ( n = 2), extended spectrum beta-lactamase producing organisms ( n = 1), Shiga-toxin producing Escherichia coli ( n = 1), and sapovirus ( n = 1). Universal donors were lost temporarily because of travel, antibiotic exposures, and transient carriage of antibiotic-resistant organisms. Conclusions: Recruiting and retaining suitable donors for FMT is challenging because of rigorous exclusions and labour-intensive screening processes. We present considerations for efficiency in donor screening, including targeting recruitment populations, expanded website self-screening, eliminating physical examinations, and streamlining post-travel risk assessment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".