Disproportionality Analysis of Nemolizumab in Patients with Atopic Dermatitis: A Real-World Pharmacovigilance Study
Bibliographic record
Abstract
Abstract: Background: Nemolizumab is approved for the treatment of moderate-to-severe atopic dermatitis in patients aged ≥12 years, combined with topical corticosteroids and/or calcineurin inhibitors, when the disease is not adequately controlled by topical prescription therapies. Given the limited duration of follow-up in clinical trials and the current lack of postmarketing surveillance data, further investigations to evaluate the long-term safety profile of nemolizumab are urgently needed. Methods: Adverse event (AE) signals were identified using disproportionality analysis with four algorithms: the reporting odds ratio (ROR), proportional reporting ratio, information component, and empirical Bayesian geometric mean. The data analyzed were from the FDA Adverse Event Reporting System, covering the period from the first quarter of 2024 to the second quarter of 2025. Results: The three most frequently reported AEs were pruritus, rash, and headache. The top three AE signals with ROR values were eczema herpeticum, pemphigoid, and dermatitis exfoliative generalized. Specific AEs included injection site hemorrhage, injection site discoloration, eyelid edema, and urticaria. Notably, sleep disorder and eosinophil count emerged as new AEs. Conclusion: These findings provide a comprehensive real-world safety overview of nemolizumab, highlighting both expected and emerging risks to inform clinical monitoring and risk management strategies during patient treatment.
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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".