Faecal microbiota transplantation for recurrent <i>C. difficile</i> infections: challenges and improvement opportunities for clinical practice and healthcare systems
Bibliographic record
Abstract
BACKGROUND: There is growing interest in faecal microbiota transplantation (FMT) as a treatment for recurrent Clostridioides difficile infection (CDI), but evidence on the diverse requirements for safe, effective and accessible services is fragmented and limited. AIMS: To identify key components of FMT provision relating to the patient care pathway, stool donor pathway and wider healthcare system, and to explore variation in practice METHODS: We conducted a narrative review of the literature and consultations with key clinical experts in the field. Evidence is drawn from high-income country contexts, with an emphasis on Australia, Canada, Italy and the United Kingdom as case example countries. RESULTS: We identify and discuss key challenges to do with healthcare capacity (workforce, FMT and stool banking facilities), donors and donations, patient access and choice of FMT delivery routes, regulation, costs and reimbursement. We also identify improvement opportunities to increase awareness of FMT and referral processes, physician training, maintaining patient registries and outcome monitoring metrics, in-country regulatory harmonisation and tackling reimbursement challenges and discuss future research needs. CONCLUSION: Effectively bringing FMT to patients in a healthcare system requires much more than just the existence of a clinically effective procedure. With FMT being a potentially effective treatment option for recurrent CDI for many patients, a well-rounded understanding of how appropriate FMT capacity can be built and nurtured is important for both healthcare providers and policymakers seeking to improve patient care.
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 | 0.032 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".