The Role of Tofacitinib in the Treatment of Acute Severe Colitis in Children
Bibliographic record
Abstract
Abstract Objectives Acute severe colitis (ASC) occurs in up to 15 percent of children with ulcerative colitis, with a high index of morbidity and mortality. Treatment includes high-dose steroids, infliximab, and salvage therapies. Unfortunately, up to 20 percent of patients may need an urgent colectomy due to treatment failure. We report our experience using tofacitinib for the treatment of six patients. Methods A retrospective review of our medical electronic records was conducted. We included every patient with ASC and treatment failure, in whom tofacitinib was used as a salvage therapy. Response, complications, and disease course were noted. Results Six patients were included with Pediatric Ulcerative Colitis Activity Index (PUCAI) scores ranging from 65 to 85 on admission, and 35 to 85 before tofacitinib was started (P 0.07). Median response time was 72 h. A median decrease of 40 points in PUCAI was noted (P 0.00001). Mean length of stay was 18 days with discharge 9 days after tofacitinib introduction. Haemoglobin, albumin, fecal calprotectin, and CRP improved after tofacitinib (P 0.02, P 0.02, P 0.025, and P 0.01, respectively). The mean follow-up was 8.5 months, four patients achieved complete remission and only one had a recrudescence of symptoms (P 0.01). One patient had a systemic Epstein-Barr virus infection prior to tofacitinib therapy, which resolved with rituximab treatment. No other complications were noted. Conclusions Tofacitinib response is rapid and impressive in children suffering from ASC, and the safety profile appears comparable to or better than other available treatments. In the future, tofacitinib should be integrated into pediatric protocols.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".