Bibliographic record
Abstract
PURPOSE OF REVIEW: Fecal microbiota transplant (FMT) has emerged as an important treatment for antibiotic resistant or recurrent Clostridium difficile infection. There has been a great deal of media coverage of the efficacy of FMT, and patients with inflammatory bowel disease (IBD) understandably wonder if this approach would also work for them. There are also instructions on 'do it yourself' FMT therapy on the web. It is important to understand whether there is evidence that this approach is effective in IBD so that we can advise our patients appropriately. RECENT FINDINGS: Systematic reviews have identified four case series involving 27 ulcerative colitis patients with a pooled remission rate of 24% (95% confidence interval (CI) = 11-45%). Two randomized controlled trials evaluating a total of 123 active ulcerative colitis patients have given conflicting results but the pooled data do suggest benefit with a number needed to treat of 6 (95% CI = 3-33). There are four case series involving 38 patients with Crohn's disease with a clinical response in 60.5% (95% CI = 28-86%). There are no randomized trials in Crohn's disease. SUMMARY: At present there are insufficient data to recommend FMT in IBD, and patients certainly should not be administering this themselves. This remains an interesting approach to treating IBD and more studies are needed to establish the optimal method of delivery as well as randomized, placebo controlled trials to establish the efficacy of 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".