Therapy resistant urticaria as a long-term symptom of an incomplete Schnitzler syndrome
Bibliographic record
Abstract
BACKGROUND: Recurring therapy resistant hives, accompanied by IgM-gammopathy, fever and joint pain can indicate Schnitzler syndrome, a rare autoimmune disorder. There is currently no approved treatment, but complete remission of symptoms can be induced with IL-1 antagonists. CASE PRESENTATION: A patient with a history of chronic urticaria presented frequently at the outpatient clinic with severe hives and was treated unsuccessfully with antihistamines and omalizumab. After several years, additional symptoms such as joint pain, recurrent fever, and IgM-gammopathy developed. After the diagnostic criteria for Schnitzler syndrome were met, treatment with anakinra was initiated and resulted in an improvement of the symptoms. Shortly after the first injection, the patient developed large and painful erythematous lesions at the injection sites, leading to discontinuation of treatment and a rapid recurrence of symptoms. Subsequently, treatment with a longer-acting IL-1 antagonist (canakinumab) was initiated, resulting in a complete remission of symptoms. CONCLUSION: This case report demonstrates that patients with urticarial symptoms that are not relieved by typical treatments should prompt repeated reassessments of the diagnosis, even years later, because gammopathy and other diagnostic criteria for Schnitzler syndrome can occur with a delay.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".