Regional variability in fecal microbiota transplantation practices: a survey of the Southern Ontario Fecal Microbiota Transplantation Movement
Bibliographic record
Abstract
Background: There is growing evidence that fecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridium difficile infection, but little guidance exists for implementation of FMT programs. The objective of this study is to describe the program characteristics and protocols of 9 planned or operating FMT programs in the Southern Ontario Fecal Microbiota Transplantation (SOFT) Movement, to help guide future FMT program implementation. Methods: A 59-item survey was administered electronically to clinical leads of the SOFT Movement on June 2, 2016. The survey evaluated 7 domains: FMT program characteristics, FMT recipients, donor screening/selection, transplant manufacturing, FMT administration, good manufacturing procedures/biosafety procedures and infection-control procedures. We used descriptive statistics to analyze quantitative data. Results: All 9 programs responded to the survey: 6 were active, 1 had FMT standard operating procedures developed but did not have clinical experience, and 2 were in the process of forming FMT programs. All 6 active programs performed FMT in adult patients with C. difficile infection. About 1300 FMT procedures were performed between 2003 and 2016. Five of the 6 operating programs administered the preparation via enema. Programs were driven primarily by physicians. All programs used universal FMT donors and followed Health Canada’s screening guidelines, with considerable variability in screening frequency (every 3–6 mo) and modality. Locations for transplant preparation and manufacturing protocols varied across programs. Stool mass for FMT ranged from 20 g to 150 g, and transplant volume ranged from 25 mL to 300 mL. Interpretation: The experience of this high-volume regional FMT network highlights current challenges in FMT program development, including a high reliance on physicians and the costly nature of donor screening. Standardization and optimization through development of regional centres of excellence for FMT donor recruitment and administration should be explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".