Bibliographic record
Abstract

 
 
 Over the past decade, Janus kinase (JAK) inhibitors have been developed for the treatment of several immune-mediated inflammatory diseases, including ulcerative colitis (UC) and Crohn’s disease (CD). The JAK-signal transducer and activator of transcription (STAT) pathway plays an essential role in coordinating the human immune response. Phosphorylation and activation of the JAK family of tyrosine kinases results in subsequent activation of intracytoplasmic STAT pathways with upregulation of inflammatory gene transcription. Blocking this signalling results in broad-spectrum immunosuppression, which is effective in the treatment of rheumatoid arthritis (RA), psoriasis, atopic dermatitis, and inflammatory bowel disease (IBD). To date, three oral, small-molecule JAK inhibitors (tofacitinib, filgotinib, and upadacitinib) have received regulatory approval in various jurisdictions globally for the treatment of moderate-to-severely active UC. It is anticipated that upadacitinib will soon become the first novel, advanced oral small molecule therapy approved for moderate-to-severely active CD. While these agents are highly effective, emerging data has highlighted potentially relevant safety signals associated with JAK inhibitors, and that the therapeutic index of these therapies may be distinct from that of monoclonal antibodies. Therefore, JAK inhibitors have a unique position in the therapeutic armamentarium for IBD. Here, we summarize the evidence supporting the use of JAK inhibitors and provide an overview of their practical applications in clinical 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.023 |
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".