Diagnosis and management of eosinophilic cellulitis (Wells' syndrome): A case series and literature review
Bibliographic record
Abstract
INTRODUCTION: Eosinophilic cellulitis (Wells' syndrome) is an inflammatory dermatitis that is often misdiagnosed as infectious cellulitis due to its similarity in presentation. Misdiagnosis leads to delay of correct treatment and inappropriate use of antibiotics. METHODS: A case series of eosinophilic cellulitis and a literature review are presented. RESULTS: Patients with Wells' syndrome may present with a variety of nonspecific symptoms, such as fever, arthralgia and malaise, as well as myriad cutaneous lesions with associated erythema, presenting as blisters, bullae, papules and/or nodules. Several treatment modalities have been used to treat eosinophilic cellulitis and have been met with variable success rates; these include systemic corticosteroids, topical corticosteroids and antihistamines, with success rates of 91.7%, 50% and 25%, respectively. CONCLUSIONS: A high degree of clinical suspicion must be exercised to diagnose this rare condition. Cellulitis with an atypical presentation or not responding to appropriate antibiotic treatment should trigger suspicion of Wells' syndrome. To date, the most successful treatment method is a short course of systemic corticosteroids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".