Systematic review with meta‐analysis: efficacy and safety of oral Janus kinase inhibitors for inflammatory bowel disease
Bibliographic record
Abstract
Summary Background Janus kinase (JAK) inhibitors represent a novel therapeutic class for treatment of inflammatory bowel disease. Aims To determine the efficacy and safety of JAK inhibitors compared to placebo for the treatment of Crohn's disease (CD) and ulcerative colitis (UC). Methods PubMed, Embase and CENTRAL were systematically searched to November 1, 2018. Randomised placebo‐controlled trials (RCTs) of JAK inhibitors in adult patients with CD or UC were eligible. Open‐label extension studies without a placebo comparator arm were excluded. Clinical, endoscopic, and safety outcomes were extracted and rates relative to placebo were pooled using a random‐effects model. Results A total of 12 RCTs (5 CD, 7 UC) were included. Patients were randomised to placebo (n = 844), tofacitinib (n = 1882), filgotinib (n = 130), peficitinib (n = 176), upadacitinib (n = 387) or TD‐1473 (n = 31). JAK inhibitor treatment was associated with induction of clinical remission in CD (RR, relative risk 1.38 [95% confidence interval CI 1.04‐1.83], P = 0.025, I2 = 14%) and UC (RR 3.07 [95% CI 2.03‐4.63], P < 0.001, I2 = 0%). In UC, JAK inhibitor treatment was associated with induction of endoscopic remission (endoscopic Mayo subscore MCSe = 0/1) (RR 2.43 [95% CI 1.64‐3.59], P < 0.001, I2 = 27%) and mucosal healing (MCSe = 0) (RR 5.50 [95% CI 2.46‐12.32], P < 0.001, I2 = 0%). JAK inhibitor treatment increased the risk of infection compared to placebo (RR 1.40 [95% CI 1.18‐1.67], P < 0.001, I2 = 0%), particularly for herpes zoster. Conclusions JAK inhibitors are effective for inducing clinical remission in CD and induction of clinical and endoscopic remission in UC, although are associated with an increased risk of infectious complications.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.045 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".