Retrospective review of drug‐induced Stevens‐Johnson syndrome and toxic epidermal necrolysis cases at a pediatric tertiary care institution
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Stevens-Johnson syndrome and toxic epidermal necrolysis represent important sources of potential mortality and morbidity in children. There is a need for more clinical data in this population to determine whether specific treatments preferentially improve outcomes. METHODS: This was a single-center retrospective review of children admitted with drug-induced Stevens-Johnson syndrome, toxic epidermal necrolysis or Stevens-Johnson syndrome/toxic epidermal necrolysis overlap at a tertiary care pediatric institution in North America from 2008 to 2018. Patients without a dermatology assessment and diagnosis were excluded. Demographic, clinical, and treatment information were abstracted and reviewed for all included patients. RESULTS: Sixteen patients were identified, 43% female (7/16), with a mean age at presentation of 10.4 ± 5.2 years. Antibiotics were implicated in 56.3% of patients (9/16) and anticonvulsants in 31.3% (5/16). Sulfamethoxazole-trimethoprim was the triggering antibiotic in 31.3% of patients. The majority of patients were treated with intravenous immunoglobulin alone (50%, 8/16) or intravenous immunoglobulin with steroids (25%, 4/16). Etanercept was added to intravenous immunoglobulin and corticosteroid in a 2-year-old patient, resulting in clinical stabilization and halting of epidermolysis. No patients died. Clinical sequelae were noted in five patients, including ocular complications (n = 4), labial adhesions (n = 1), and persistent skin dyspigmentation (n = 3). CONCLUSIONS: Our results highlight that sulfamethoxazole-trimethoprim is an important cause of Stevens-Johnson syndrome-toxic epidermal necrolysis in children. Mortality was reassuringly low, but ocular sequelae were an important cause of morbidity. More data are needed to help determine whether specific treatments including etanercept may provide mortality or morbidity benefit in pediatric populations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".