Analysis of publicly available adverse events reported for pediatric patients treated with <scp>Janus kinase</scp> inhibitors
Bibliographic record
Abstract
Janus kinase inhibitors (JAKi) are drugs that block tyrosine kinases responsible for transducing cytokine signals. The first JAKi was approved by the US Food and Drug Administration (FDA) in 2011 to treat rheumatoid arthritis in adults. A pediatric indication was not approved until 8 years later, for acute graft-versus-host disease. Since then, topical and oral formulations have gained FDA approval for pediatric patients with dermatologic diseases. While increasing evidence supports the safety of these medications in adults, data are limited in children. We sought to determine whether JAKi adverse events (AEs) as reported in clinical trials and via postapproval pharmacovigilance services are comparable in adult and pediatric patients. Pharmacovigilance data were extracted from the FDA's Adverse Event Reporting System and the Canada Vigilance Adverse Reaction Online Database for baricitinib, upadacitinib, abrocitinib, ruxolitinib, and tofacitinib. The pooled data were analyzed to detect the most common AEs for specific JAKi and for the drug class. We assessed 399,649 AEs from 133,216 adults and 2883 AEs from 955 patients under 18 years old and identified slightly different AE profiles for the two age groups. Both populations had increased risk for infections and gastrointestinal AEs. However, pediatric patients reported a higher proportion of blood and lymphatic disorders, while reports of nervous system and musculoskeletal/connective tissue disorders were more common in adults. The spectrum of AEs extracted from pharmacovigilance reports was similar to clinical trials. The JAKi AE profiles we observed may prove helpful in counseling patients and their parents before starting therapy and for monitoring once patients are on therapy.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".