Towards optimising and standardising donor screening for faecal microbiota transplantion
Bibliographic record
Abstract
infection or within microbiome intervention clinical trials. Donor screening is of paramount importance given the potential risk of pathogen transmission-a feared complication. While rare in practice, documented cases of FMT-associated infections have resulted in significant morbidity and even mortality. Despite the importance of screening, evidence-based approaches to developing donor-screening protocols are lacking. Inadequate screening for transmissible pathogens may lead to infections in recipients, while overly cautious screening for pathogens with negligible transmission potential could strain healthcare resources and unnecessarily exclude donors, who are already in limited supply. This review aimed to evaluate the evidence underpinning current FMT donor screening protocols. We began by comparing protocols from major FMT guidelines and manufacturers, highlighting their differences in lists of screened pathogens, laboratory assays and clinical characteristics used for donor selection. We critically appraised the existing literature on transmission dynamics for pathogens. These findings were incorporated into a Delphi process with an expert panel group to develop a rational and streamlined screening approach. We further emphasised the importance of maintaining transparency with regard to donor recruitment, screening, monitoring and traceback record keeping. Finally, we explored future directions in donor screening, including approaches to monitoring emerging pathogens and the potential for integration of new technologies, such as metagenomic assays, to enhance and refine donor selection.
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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 | 0.254 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".