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Record W4297537206 · doi:10.14740/jmc3992

Steven-Johnson Syndrome: A Rare but Serious Adverse Event of Nivolumab Use in a Patient With Metastatic Gastric Adenocarcinoma

2022· article· en· W4297537206 on OpenAlexvenueno aff
Eltaib Saad, Pabitra Adhikari, Drashti Antala, Ahmed Abdulrahman, Valiko Begiashvili, Khalid Mohamed, Elrazi Ali, Qishou Zhang

Bibliographic record

VenueJournal of Medical Cases · 2022
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsNivolumabMedicineDiscontinuationToxic epidermal necrolysisAdverse effectDermatologyAdenocarcinomaCancerGastric adenocarcinomaOncologySurgeryInternal medicineImmunotherapy

Abstract

fetched live from OpenAlex

Nivolumab is a humanized monoclonal anti-programmed cell death receptor-1 (PD-1) antibody that has been authorized for use in the treatment of advanced malignancies. Cutaneous reactions are the most common immune-related adverse events reported with anti-PD-1 agents, and they range broadly from mild localized reactions to rarely severe or life-threatening systemic dermatoses. The occurrence of Steven-Johnson syndrome (SJS) or toxic epidermal necrolysis (TEN) with nivolumab use is an exceedingly rare phenomenon that was only documented in a handful of cases in the current literature, but it deserves careful attention as SJS/TEN may be associated with fatal outcomes. We present a case of nivolumab-induced SJS/TEN in a middle-aged female patient with metastatic gastric adenocarcinoma that was successfully treated with immunosuppressive therapy and supportive care. Prompt recognition of SJS/TEN with discontinuation of nivolumab is warranted when SJS/TEN is suspected clinically. Multidisciplinary management in a specialized burn unit is the key to improving outcomes of SJS/TEN.

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.

How this classification was reachedexpand

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.276
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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