Medical Therapy for Acute Severe Ulcerative Colitis: A Systematic Review With Meta-analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: Acute severe ulcerative colitis (ASUC) is a medical emergency associated with high morbidity and mortality. We aimed to assess the efficacy and safety of medical treatments for ASUC. METHODS: MEDLINE, EMBASE, and CENTRAL were searched to November 14, 2024 for randomized controlled trials (placebo or active comparator), and comparative cohort studies that evaluated medical therapies for hospitalized adults with ASUC. Primary outcomes were colectomy rate at discharge and at 3 and 12 months. Safety outcomes included adverse events, serious adverse events, and infections. Pooled risk ratios (RR) and 95% confidence intervals (CI) were calculated. RESULTS: = 15%). There was no significant difference between accelerated and standard dosing regimens of IFX for colectomy at discharge or at 3 months. Tofacitinib in combination with intravenous corticosteroids was significantly superior to intravenous corticosteroids alone for induction of clinical response at Day 7; however, there was no difference in colectomy rate or infections at 3 months. CONCLUSIONS: IFX may be more efficacious than cyclosporine A for the treatment of ASUC. Janus kinase inhibitors are promising treatment options in select individuals. Future studies should explore the efficacy of other advanced therapies.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".