Schnitzler syndrome without monoclonal gammopathy: a systematic review and case presentation
Bibliographic record
Abstract
Schnitzler syndrome (SS) is a rare autoinflammatory disorder that presents with chronic urticarial rash, neutrophilic urticarial dermatosis, monoclonal IgG or IgM gammopathy, recurrent fever, joint and/or bone pain and enlarged lymph nodes, spleen and/or liver. Several cases in the literature have described patients with incomplete variants of SS that lack monoclonal gammopathy. This systematic review aims to analyse the current literature regarding the features of Schnitzler-like syndrome (SLS; i.e. patients with clinical features of SS but without the obligate Strasbourg criterion of monoclonal gammopathy) and their treatment outcomes; we also report our own case of a patient with SLS. A systematic search of MEDLINE, Embase, Scopus and PubMed was performed (PROSPERO: CRD42024548245). All articles in English or French were included, and no publication date restrictions were applied. All articles with original data, clinical features and treatment outcomes were included. Two reviewers independently conducted screenings and conflicts were resolved by a third reviewer if necessary. In total, 15 studies (13 case reports and 2 case series) met inclusion criteria, in addition to 1 patient's case from our own experience, resulting in a total of 18 patients. The majority of our population were male patients (56%) with a median age at diagnosis of 57.5 years (interquartile range 44.8-62.8). Most of the patients had absent monoclonal gammopathy (n = 14; 78%), and four (22%) had delayed-onset monoclonal gammopathy. Antihistamines were the most common treatment (n = 18; 100%), followed by anakinra (n = 14; 78%), prednisone (n = 9; 50%), unspecified systemic corticosteroids (n = 8; 44%), omalizumab (n = 8; 44%), colchicine (n = 5; 28%), dapsone (n = 6; 33%), ciclosporin (n = 4; 22%), canakinumab (n = 4; 22%), hydroxychloroquine (n = 2; 11%) and thalidomide (n = 1; 6%). Of the 14 patients treated with anakinra, 93% (n = 13/14) achieved a complete response. Our findings raise the hypothesis that monoclonal gammopathy may not be an obligate criterion for SS. We propose this as a consideration for future consensus discussions, emphasizing the potential benefits of early recognition and timely interleukin-1 inhibitor therapy to improve patient outcomes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".