A systematic review of treatment of drug-induced Stevens-Johnson syndrome and toxic epidermal necrolysis in children.
Bibliographic record
Abstract
Stevens-Johnson (SJS) and Toxic Epidermal Necrolysis (TEN) are two uncommon mucocutaneous diseases usually considered as severe drug reactions and are characterized by different grades of epidermal necrosis. Several treatment modalities have been proposed with variable results but the lack of controlled studies makes difficult to analyze them objectively especially in children. All publications describing management for SJS and TEN in children were searched in MEDLINE, EMBASE, and the Cochrane Library. Reports included were divided in two categories: A, studies with 5 or more patients and observational studies; and B, reports with less than 5 patients. A formal meta-analysis was not feasible. Description was made using central tendency measures. From 1389 references only 31 references with a total of 128 cases were included, 88 category A and 40 category B. The 4 main treatment modalities were: intravenous immunoglobulin (IVIG), steroids (prednisolone, methylprednisolone, dexamethasone), dressings with or without surgical debridement, and support treatment alone. Miscellaneous treatments: Of 12 patients, 3 received ulinastatin, 4 patients plasmapheresis, 2 patients IV pentoxifylline and the last three patients received different treatment each (cyclosporine, methylprednisone/G-CSF and methylprednisolone/IVIG). Patients receiving IVIG and steroids showed similar findings while patients treated with dressing and support treatment alone, reported both longer time to achieve remission and hospitalization stays and appear to be associated with more complications and deaths. There is scant quality literature about management of SJS and TEN in children. Steroids and IVIG seem to improve the outcome of SJS and TEN patients but results from different reports are variable. Patients treated only with care support seem to have higher morbidity and mortality. Further studies are necessary to define optimal management.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".