Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Changes in the colonic microbiota may play a role in the pathogenesis of ulcerative colitis (UC) and restoration of healthy gut microbiota may ameliorate disease. A systematic review and meta-analysis was conducted to assess fecal microbiota transplantation (FMT) as a treatment for active UC. METHODS: A literature search was conducted to identify high-quality studies of FMT as a treatment for patients with UC. The primary outcome was combined clinical remission and endoscopic remission or response. Secondary outcomes included clinical remission, endoscopic remission, and serious adverse events. Odds ratios with 95% confidence intervals (CIs) are reported. RESULTS: Overall, 4 studies with 277 participants were eligible for inclusion. Among 4 randomized controlled trials, FMT was associated with higher combined clinical and endoscopic remission compared with placebo (risk ratio UC not in remission was 0.80; 95% CI: 0.71-0.89) with a number needed to treat of 5 (95% CI: 4-10). There was no statistically significant increase in serious adverse events with FMT compared with controls (risk ratio adverse event was 1.4; 95% CI: 0.55-3.58). CONCLUSIONS: Among randomized controlled trials, short-term use of FMT shows promise as a treatment to induce remission in active UC based on the efficacy and safety observed. However, there remain many unanswered questions that require further research before FMT can be considered for use in clinical practice.
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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".