Toxic Skin Reactions Should Be Differentiated from Allergic Reactions to Chemotherapeutic Drugs in Children: A Case Series and Review of the Literature
Bibliographic record
Abstract
Abstract: Background: Chemotherapeutic drugs can lead to a wide spectrum of cutaneous findings, ranging from nonimmune toxic reactions to severe immune-mediated hypersensitivity reactions. The aim of this study was to evaluate the clinical, histopathological features, and prognosis of toxic skin reactions to chemotherapeutic drugs and to compare them with characteristics of immune-mediated reactions in children with malignancies. Methods: The medical records of all children with cancer who experienced skin reactions after chemotherapy administration and diagnosed as a toxic skin reaction between 2010 and 2022 were retrospectively analyzed. The diagnosis was re-evaluated and differentiated from other similar disorders by using clinical manifestations, photodocumentation, and histopathological findings. Results: A total of 17 children aged 2–17 years were involved: toxic erythema of chemotherapy (TEC) in 14 children, methotrexate-induced epidermal necrosis in 2 children, and toxic epidermal necrolysis (TEN)-like TEC in 1 child. The most commonly implicated drug was methotrexate. Most patients recovered rapidly after drug cessation and supportive measures. In 10 of the 17 patients, reintroduction of the culprit chemotherapeutic drug at reduced doses or increased dosage intervals was possible without any recurrence. Six patients could not receive further doses since they deceased due to sepsis and other complications. Conclusions: Cutaneous toxic eruptions to chemotherapeutic drugs may present with a severe phenotype resembling Stevens–Johnson syndrome/TEN. An accurate diagnosis prevents potentially harmful therapeutic interventions, withholding of chemotherapy, and erroneous assignment of drug allergies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".