Janus kinase inhibitor—Tofacitinib associated with pemphigus: an analysis of the FDA adverse event reporting system data
Bibliographic record
Abstract
OBJECTIVES: To establish the association between the therapy of Janus kinase inhibitors and the adverse event of pemphigus in patients with rheumatologic and inflammatory disorders. METHODS: A disproportionality analysis using multi-item gamma Poisson shrinker was conducted to identify signals between medication and adverse events within the FDA Adverse Event Reporting System. RESULTS: The spontaneous reporting system contained 3,032 pemphigus reports associated with two Janus kinase inhibitors, namely Tofacitinib and Upadacitinib. The year/reporter/geographic area/country/age/sex/indication with the highest number of cases were the year of 2021, physician, North America, Canada, age between 40-49, female and rheumatoid arthritis, respectively. A significant signal was detected in the Tofacitinib group. CONCLUSION: Pemphigus, a rare and potentially fatal adverse event, was found to occur more frequently in patients receiving Tofacitinib. High-risk individuals were identified as female, age between 40-49, or with rheumatoid arthritis. Medication, adverse events, and underlying disease conditions were identified as potential contributing factors. Rheumatology and dermatology specialists should exercise increased vigilance in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".