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Record W4292079462 · doi:10.1177/2050313x221117887

Reactive infectious mucocutaneous eruption – repeat etanercept after intravenous immunoglobulin: A case report

2022· article· en· W4292079462 on OpenAlexaff
Rochelle Tonkin, Malika A. Ladha, Nicole Johnson, William F. Astle, Ami Britton, Neil H. Shear, Luis Murguía-Favela, Michele Ramien

Bibliographic record

VenueSAGE Open Medical Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Bullous Skin Diseases
Canadian institutionsHealth Sciences CentreUniversity of TorontoAlberta Children's HospitalSunnybrook Health Science CentreUniversity of Calgary
Fundersnot available
KeywordsMucocutaneous zoneMedicineEtanerceptDermatologyImmunologyIntravenous Immunoglobulin TherapyAntibodyDiseasePathologyRheumatoid arthritis

Abstract

fetched live from OpenAlex

Reactive infectious mucocutaneous eruption is a recently distinguished mucosal-predominant blistering eruption triggered by respiratory infections. We describe a previously healthy 11-year-old Black female with rapidly progressive mucocutaneous blistering after prodromal respiratory infection symptoms. Reactive infectious mucocutaneous eruption was suspected and treated with systemic corticosteroids followed by etanercept. Twenty-four hours after etanercept, the diagnosis of multisystem inflammatory syndrome in children was raised and intravenous immunoglobulin was given. Rapidly worsening mucocutaneous disease ensued but was controlled by a second dose of etanercept. Our case highlights the following: (1) the novel observation of possible interaction/neutralization of etanercept by intravenous immunoglobulin, (2) the challenging differential diagnosis of multisystem inflammatory syndrome in children for reactive infectious mucocutaneous eruption patients in the Coronavirus disease 2019 (COVID-19) pandemic, and (3) the role of early treatment to prevent dyspigmentation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.291
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2022
Admission routes1
Has abstractyes

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