Safety evaluation of ILaris: a real-world analysis of adverse events based on the FAERS Database
Bibliographic record
Abstract
There is a lack of real-world studies on the safety of Ilaris in large populations. The purpose of this study was to investigate adverse events (AEs) associated with Ilaris using data from the FDA Adverse Event Reporting System (FAERS) database to guide clinical use. We evaluated retrospectively extracted reports of AEs from the FAERS database between the first quarter of 2009 and the second quarter of 2024. The presence of a significant association between Ilaris and AEs was assessed by using disproportionality analyses including ROR,PRR,BCPNN,MGPS. After evaluating 14,691,170 data, 7968 ILaris-associated AEs were obtained after removing duplicates and unspecified sex items. A number of AEs were finalized through the study, including Common Pyrexia、Condition Aggravated、Influenza, and unexpected signals not listed in the drug insert, such as Pulmonary Thrombosis、Hepatomegaly、Blood Lactate Dehydrogenase Increased、Splenomegaly、Appendicitis. Ilaris induced AEs involving 27 system organ classes (SOCs). There were gender differences in AEs signaling associated with Ilaris. It is critical for healthcare professionals to closely monitor patients for symptoms (such as pulmonary thrombosis, Hepatomegaly, Blood Lactate Dehydrogenase Increased, Splenomegaly, Appendicitis) and other adverse events during treatment.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".