Positioning of tofacitinib in treatment of ulcerative colitis: a global perspective
Bibliographic record
Abstract
Tofacitinib has emerged as a useful drug for the treatment of ulcerative colitis (UC). There is an unmet need for cost-effective, non-immunogenic drugs with a safe adverse effect profile to treat patients with ulcerative colitis. In the present review, we evaluate the available literature to inform the appropriate positioning of tofacitinib in the current drug landscape and identify subsets where its use should be done with caution. Tofacitinib is helpful in the treatment of patients where the standard conventional or biological therapies have failed or were not tolerated. With lower costs of the generic drug than the biologicals (or biosimilars), it could be an important therapy in low- to middle-income countries. The risk of infections, especially Herpes Zoster and tuberculosis, needs to be addressed before initiation. Tofacitinib should be avoided in patients with venous thromboembolism and cardiovascular disease risk factors. Due to limited evidence, the use is not recommended in pregnancy, while it should be used with caution in elderly citizens. Future trials should look into the head-to-head comparison of tofacitinib with biologicals. The role of tofacitinib in acute severe colitis needs evaluation with comparative trials with current standards of care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".