Fecal microbiota transplantation: therapeutical approaches and regulatory framework
Bibliographic record
Abstract
Since Ilya Metchnikoff’s studies, both medical science and clinical practice have accumulated a large amount of evidence that human intestinal microbiota possesses a unique characteristics for our existence. However, only recently, scientists have achieved the actual breakthrough in this field of human physiology, and we start to understand the precise mechanisms of the complex interplay of microbial activity with human homeostasis and discover numerous new functions of intestinal microbes. In this regard, a novel medical technology evolves since 1958, so called fecal microbiota transplantation (FMT), which is the administration of donor feces to the patients suffering from different kinds of diseases. Such infusion of donor feces restores the natural balance of gut commensal germs. FMT is most efficacious in severe or recurrent Clostridium difficile infection. FMT has been also reported to cure diarrhea and constipation caused by different conditions, such as multiple sclerosis, Crohn ’s disease etc. In the U.S. and Canada as well as in other countries FMT is of uttermost interest of not merely clinical practitioners but lately also of regulatory authorities. The paper also addresses the possibility of application the Russian pharmaceutical legislation to FMT.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".