Post-acute phase and sequelae management of epidermal necrolysis: an international, multidisciplinary DELPHI-based consensus
Bibliographic record
Abstract
BACKGROUND: Long-term sequelae are frequent and often disabling after epidermal necrolysis (Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN)). However, consensus on the modalities of management of these sequelae is lacking. OBJECTIVES: We conducted an international multicentric DELPHI exercise to establish a multidisciplinary expert consensus to standardize recommendations regarding management of SJS/TEN sequelae. METHODS: Participants were sent a survey via the online tool "Survey Monkey" consisting of 54 statements organized into 8 topics: general recommendations, professionals involved, skin, oral mucosa and teeth, eyes, genital area, mental health, and allergy workup. Participants evaluated the level of appropriateness of each statement on a scale of 1 (extremely inappropriate) to 9 (extremely appropriate). Results were analyzed according to the RAND/UCLA Appropriateness Method. RESULTS: Fifty-two healthcare professionals participated. After the first round, a consensus was obtained for 100% of 54 initially proposed statements (disagreement index < 1). Among them, 50 statements were agreed upon as 'appropriate'; four statements were considered 'uncertain', and ultimately finally discarded. CONCLUSIONS: Our DELPHI-based expert consensus should help guide physicians in conducting a prolonged multidisciplinary follow-up of sequelae in SJS-TEN.
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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 | 0.183 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".