Pediatric <scp>S</scp>weet syndrome. A retrospective study
Bibliographic record
Abstract
INTRODUCTION: Sweet syndrome (SS) is a relatively rare pediatric diagnosis, with fewer than 80 pediatric cases reported in the literature, characterized by tender erythematous plaques and nodules associated with systemic inflammation. MATERIALS AND METHODS: We retrospectively reviewed the charts of pediatric patients diagnosed with SS both clinically and histologically at our reference hospital between the years of 2000 and 2012. Clinical, laboratory, and pathologic data were analyzed. RESULTS: We found five patients; four were male, aged between 9 and 14 years. All had fever, elevated markers of systemic inflammation, and typical skin lesions. SS was associated with underlying hematologic malignancy in one patient; all-trans retinoic acid in another; infection in two patients; and in one patient, no identifiable cause was found. Three of the five patients treated with systemic corticosteroids had excellent response, and two had recurrences and received additional treatment with dapsone and saturated solution of potassium iodide. CONCLUSIONS: Sweet syndrome is an extremely rare diagnosis in children. It is associated with the same conditions as in adults, but it is more frequently associated with infections than malignancies. In general, prognosis is good, but recurrences occur and second-line treatment may be needed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".