Combination Therapy With Tofacitinib Plus Intensive Granulocyte and Monocyte Adsorptive Apheresis as Induction Therapy for Refractory Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND: The use of monotherapy with intensive granulocyte and monocyte adsorptive apheresis (GMA) or a Janus kinase (JAK) inhibitor has been limited to patients with refractory ulcerative colitis (UC). The efficacy and safety of combination therapy with tofacitinib (TOF) plus intensive GMA (two sessions per week) for refractory UC have not been evaluated. METHODS: This retrospective study evaluated the 10-week efficacy of combination therapy with TOF plus intensive GMA in patients with refractory UC. RESULTS: Of seven patients who received a combination therapy with TOF plus intensive GMA, 71.4% achieved clinical remission at 10 weeks. The percentages of patients with mucosal healing and complete mucosal healing at 10 weeks were 100% and 42.9%, respectively. The mean full Mayo score and endoscopic subscore at baseline were 8.71 ± 0.80 and 2.4 ± 0.2, respectively, and the corresponding values at 10 weeks were 1.57 ± 0.48 and 0.6 ± 0.2 (P < 0.01), respectively. Adverse events of an orolabial herpes and temporary increase in creatinine phosphokinase (CK) and triglyceride were observed in three patients. CONCLUSIONS: Based on these outcomes, combination therapy with TOF plus intensive GMA was well tolerated and may be useful for induction of clinical remission in patients with refractory UC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".