Review article: guide to tofacitinib dosing in patients with ulcerative colitis
Bibliographic record
Abstract
BACKGROUND: Tofacitinib is an oral small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). The induction dose is 10 mg twice daily (b.d.), whilst for maintenance therapy, the lowest effective dose should be used. AIM: To examine published evidence on the two tofacitinib dosing strategies used in UC treatment, including expert interpretation of the data and how they could inform clinical practice. METHODS: The use of tofacitinib 5 or 10 mg b.d. was assessed using data from the tofacitinib UC clinical programme in the context of different clinical scenarios. We include experts' opinions on the clinical implications of dose adjustment to inform the benefit/risk of using tofacitinib 5 or 10 mg b.d., based on clinical scenarios and real-world data. RESULTS: Factors to consider when adjusting the tofacitinib dose include disease severity, comorbidities and previous biological exposure. The endoscopic subscore can determine whether a patient is a good candidate for dose reduction. Following disease relapse, the response can be recaptured in a substantial number of patients with a dose increase. Furthermore, data are now published showing real-world use of tofacitinib and, so far, these are consistent with data from the clinical trials. CONCLUSION: Clinicians must consider the benefit/risk balance of tofacitinib 10 versus 5 mg b.d. in terms of dose-related side effects, as well as the safety implications of undertreating active disease. All patients should be closely monitored for disease relapse following dose reduction or interruption for early recapture of response.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.030 |
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".