The use of Faecal Microbiota Transplantation (FMT) in Europe: A Europe-wide survey
Bibliographic record
Abstract
BACKGROUND: Faecal microbiota transplantation (FMT) is an emerging treatment modality, but its current clinical use and organisation are unknown. We aimed to describe the clinical use, conduct, and potential for FMT in Europe. METHODS: We invited all hospital-based FMT centres within the European Council member states to answer a web-based questionnaire covering their clinical activities, organisation, and regulation of FMT in 2019. Responders were identified from trials registered at clinicaltrials.gov and from the United European Gastroenterology (UEG) working group for stool banking and FMT. FINDINGS: infection (CDI) as indication, 791 (42%) with experimental indications, and 6 (0•3%) unaccounted for. Adjusted to population size, 0•257 per 100,000 population received FMT for CDI and 0•189 per 100,000 population for experimental indications. With estimated 12,400 (6,100-28,500) annual cases of multiple, recurrent CDI and indication for FMT in Europe, the current European FMT activity covers approximately 10% of the patients with indication. The participating centres demonstrated high safety standards and adherence to international consensus guidelines. Formal or informal regulation from health authorities was present at 21 (68%) centres. INTERPRETATION: FMT is a widespread routine treatment for multiple, recurrent CDI and an experimental treatment. Embedded within hospital settings, FMT centres operate with high standards across Europe to provide safe FMT. A significant gap in FMT coverage suggests the need to raise clinical awareness and increase the FMT activity in Europe by at least 10-fold to meet the true, indicated need. FUNDING: NordForsk under the Nordic Council and Innovation Fund Denmark (j.no. 8056-00006B).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".