Something Old, Something New, Something Borrowed…
Bibliographic record
Abstract
Although knowledge of the complex interactions within our intestinal microbiota is relatively new, fecal microbiota transplantation (FMT) has been practiced for a long time. For more than a millennium, human fecal suspensions have been used to treat various ailments. The first literary evidence of oral fecal administration for the treatment of food poisoning and diarrhea dates back to the fourth century in China as detailed in the first Chinese handbook of emergency medicine written by Ge Hong (1). Later, in the 16th century, Li Shizhen described the use of several different human stool preparations for the treatment of various gastrointestinal diseases (1). The first report of FMT for the treatment of pseudomembranous colitis dates back to 1958 (2). In this case series involving four patients, all responded spectacularly. In 1983, Schwan et al (3) published the first report of FMT in a patient with a confirmed Clostridium difficile infection (CDI). Since then, the experiences of more than 250 CDI patients treated with FMT has been published in the medical literature, with an overall success rate of approximately 90% (4,5)...
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".