Corticosteroids in Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis
Bibliographic record
Abstract
OBJECTIVE: To review the evidence for the use of steroids in adults presenting with Stevens-Johnson Syndrome (SJS), toxic epidermal necrolysis (TEN), or overlap. DATA SOURCES: EMBASE (1974 to April 2014), MEDLINE (1946 to April 2014), Cochrane Database of Systematic Reviews, and International Pharmaceutical Abstracts (1970 to January 2014) were searched using the terms: prednisone, methylprednisolone, dexamethasone, prednisolone, steroids, glucocorticoids, corticosteroids, Stevens-Johnson Syndrome, toxic epidermal necrolysis, and SJS/TEN overlap. STUDY SELECTION AND DATA EXTRACTION: English-language, full reports of experimental and observational studies were included. Bibliographies from pertinent publications were reviewed for additional references. Prespecified outcomes included survival, survival to discharge, hospitalization without intensive care, length of intensive care stay, duration of hospitalization, ophthalmological complications, infection rates, and adverse events. DATA SYNTHESIS: Six studies that used steroids for SJS, TEN, and/or overlap were included. All studies were retrospective cohort studies with no case-control or cross-sectional studies; 5 studies reported on steroid doses, and 2 studies reported time from disease onset to steroid use (2-4 days). Only 1 of 6 studies reported a statistically significant impact on mortality with steroids use (odds ratio = 0.4; 95% CI = 0.2-0.9). Adverse event rates were not reported in any of the studies. CONCLUSIONS: A review of the current evidence reveals a need for prospective, randomized controlled studies to provide more definitive conclusions on steroid use in patients with SJS, TEN, and/or overlap.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".