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Record W4402549751 · doi:10.1007/s40257-024-00889-6

Update on Stevens–Johnson Syndrome and Toxic Epidermal Necrolysis: Diagnosis and Management

2024· review· en· W4402549751 on OpenAlex
Hemali Shah, Rose Parisi, Eric Mukherjee, Elizabeth J. Phillips, Roni P. Dodiuk‐Gad

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Clinical Dermatology · 2024
Typereview
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Human Genome Research InstituteMedical Research CouncilNational Institute of General Medical SciencesNational Institute of Allergy and Infectious DiseasesNational Health and Medical Research CouncilNational Institutes of Health
KeywordsToxic epidermal necrolysisMedicineDermatologyPharmacotherapyErythrodermaIntensive care medicinePsychiatry

Abstract

fetched live from OpenAlex

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are the most severe cutaneous adverse reactions that are typically drug-induced in adults. Both SJS and TEN have high morbidity and mortality rates. SJS/TEN imposes clinical challenges for physicians managing patients suffering from this condition, both because it is rare and because it is a rapidly progressing systemic disease with severe cutaneous, mucosal, and systemic manifestations. Although many cases of SJS/TEN have been reported in the literature, there is no consensus regarding diagnostic criteria or treatment. Significant progress has been made in understanding its genetic predisposition and pathogenesis. This review is intended to provide physicians with a comprehensive but practical SJS/TEN roadmap to guide diagnosis and management. We review data on pathogenesis, reported precipitating factors, presentation, diagnosis, and management SJS/TEN focusing on what is new over the last 5 years.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.442
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it