Wells Syndrome in Children: Case Study and Differential Diagnostics
Bibliographic record
Abstract
Background . Wells syndrome (eosinophilic cellulitis) is recurrent granulomatous dermatitis with peripheral blood eosinophilia. This is extremely rare pathology, therefore, there are no reliable epidemiological data on its prevalence. Only about 200 cases were recorded worldwide and 30 of them among children according to the meta-analysis (2012). The disease is mostly sporadic, there are rare family cases, according to the results of little number of scientific publications. Clinical Case Description . The clinical case of Wells syndrome in female 4 years old patient is presented. Clinical findings included symmetrical skin lesions, nodes and large irregular edematous plaques of red-purple color with clear fluid vesicles on its surface. The disease had wavy course: rashes have recovered spontaneously over 7–10 days, new elements appeared alongside with feeling unwell, fever up to 37,8°C and abdominal pain. Similar clinical findings of rashes were observed in paternal relatives of the child. Conclusion . Differential diagnostics of Wells syndrome should be carried out with skin granulomatous diseases and hypereosinophilic syndrome that may be characterized by similar clinical findings. Verification of Wells syndrome diagnosis is complicated due to its rareness, low awareness of dermatologists and pediatricians about this pathology, as well as ignoring the need to carry out histological tests during the disease exacerbation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".