The regulation of fecal microbiota for transplantation: An international perspective for policy and public health
Bibliographic record
Abstract
Clostridium difficile is the most common hospital-acquired pathogen in the US, and recurrent C. difficile infection (CDI) is a major public health issue. Twenty per cent of CDI patients experience recurrence, and their risk of recurrence rises with each failure to achieve clinical resolution. Fecal microbiota transplantation (FMT) is a remarkably efficacious treatment for recurrent CDI. However, national health agencies are grappling with the appropriate regulatory paradigm to apply to this innovative treatment. Current FMT regulations in the US, Canada, Western Europe, Australia, and China are in varying degrees of flux, although many regulators are choosing to apply the drug and biologic framework. FMT regulations should allow recurrent CDI patients safe access to this treatment as research continues. Regulating FMT like a drug or biologic, although most convenient from a legal perspective, overly restricts access while under-regulating the methods by which the stool is screened, processed, stored, and used. Human tissue and tissue-based products regulations could achieve the desired level and kind of oversight, but fecal microbiota for transplantation fail to meet applicable statutory definitions. A custom regulatory solution would be more appropriate, but many pathways that regulators may take to achieve this goal require time and resources for health agencies to develop.
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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 | 0.064 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.063 | 0.035 |
| Insufficient payload (model declined to judge) | 0.019 | 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".