Faculty Opinions recommendation of Clinical features of Stevens-Johnson syndrome and toxic epidermal necrolysis.
Bibliographic record
Abstract
BACKGROUND: Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare, but these conditions are associated with high mortality. There have been few reports of SJS and TEN in children. The aim of this study was to evaluate the clinical features and outcomes of SJS and TEN in a group of Japanese children.METHODS: We retrospectively reviewed pediatric cases of SJS and TEN, from 2000 to 2015.RESULTS: We identified 12 pediatric cases of SJS and three of TEN. Six (all SJS) were caused by infection, and eight of the cases (SJS, n = 5; TEN, n = 3) were drug induced. Respiratory complications were the most common in terms of organ involvement, followed by hepatitis and gastrointestinal symptoms. Thirteen patients were treated with systemic corticosteroids, and two patients were treated with supportive therapy only. Concomitant with corticosteroid, four patients were given i.v. immunoglobulin. One patient with severe TEN was treated with systemic corticosteroids combined with plasmapheresis and cyclosporine. None of the present patients died. One patient with TEN had severe sequelae, with bronchiolitis obliterans and ocular involvement.CONCLUSIONS: SJS/TEN are rare, but are associated with severe complications. General pediatricians need to have up-to-date information regarding these conditions. The present study provides insights into the confirmation of the risk of SJS/TEN as well as the treatment of these diseases.© 2018 Japan Pediatric Society. PMID: 29888432
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.429 | 0.214 |
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".