Sweet Syndrome in an Adolescent Patient With Differentiation Syndrome Secondary to Promyelocytic Leukemia Treatment With All-Trans Retinoic Acid
Bibliographic record
Abstract
Sweet syndrome (SS) is an acute febrile neutrophilic dermatosis that is histologically characterized by an infiltration of the dermis by neutrophils. A 12-year-old adolescent female patient recently diagnosed with acute promyelocytic leukemia presented with fever and was hospitalized for antibiotic management after 22 days of being treated with a treatment protocol based on daunorubicin, all-trans retinoic acid (ATRA), and prophylaxis with dexamethasone, the patient developed erythematous skin lesions located mostly on the extremities. Lesions evolved into painful subcutaneous nodules, and one lesion evolved into a 2.5-cm blister with a purple and necrotic base. A skin biopsy was performed and showed neutrophilic dermatosis which confirmed the diagnosis of SS. The patient's clinical features complied with criteria for differentiation syndrome complicated by shock. Two days after ATRA was suspended, the patient presented resolution of the fever and skin lesions. SS is a rare neutrophilic dermatosis secondary to an innate immune disorder classified into four categories: classical (idiopathic), para-inflammatory, paraneoplastic or pregnancy-related. SS has been described in patients with acute myeloid leukemia in adults secondary to the use of drugs such as ATRA or as a part of a paraneoplastic syndrome. SS can occur exceptionally in children with myeloid leukemia secondary to the use of drugs such as ATRA.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".