Infectious side effects of baricitinib: a big data analysis based on VigiBase
Bibliographic record
Abstract
Abstract Background and aim : baricitinib is an inhibitor of Janus-associated kinase (JAK) subtypes 1 and 2 with various effects on intracellular signaling pathways. Approved for rheumatoid arthritis (RA), and recently used for severe COVID-19, the prevalence of infections associated with baricitinib treatment has not been widely reported. We aimed to analyze the prevalence of infectious side effects in patients treated with baricitinib. Methods: using the WHO global pharmacovigilance database (VigiBase), we carried out an extensive data analysis of approximately 300 patients treated with baricitinib. The prevalence of infectious side effects was our main focus. Patients treated with baricitinib for the indication of severe COVID-19 were excluded. Results : the most prevalent infectious side effect of baricitinib was oral herpes (IC025 of 4.36) and herpes zoster infection (IC025 of 4.159). On the contrary, gastrointestinal viral infections, infectious pleural effusions and various viral pneumonias had low prevalence rates (IC025 values of 0.173; 0.093; and 0.188, respectively). Conclusion: based on a big data analysis, baricitinib was associated with infectious complications where oral herpes and herpes zoster infections shown to be more prevalent than previously reported. The immunomodulatory effect of baricitinib including cytokine effects on immune cells and the inhibition of numerous growth factors and cytokines such as IL-2, suppressing both innate and adaptive immune responses, are the most comprehensive mechanisms behind such side effects. Given the clinical indications for baricitinib (RA) and the current use in severe COVID-19, cautious approach should be taken before introducing baricitinib particularly in immunosuppressed patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".