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Record W3091927665 · doi:10.1016/j.cjco.2020.10.003

Rapid Response to Cytokine Storm Inhibition Using Anakinra in a Patient With COVID-19 Myocarditis

2020· article· en· W3091927665 on OpenAlexaff
Cvetan Trpkov, Paul MacMullan, Patricia Feuchter, Rahim Kachra, Bobak Heydari, Naeem Merchant, Michael Bristow, James A. White

Bibliographic record

VenueCJC Open · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersSiemens Healthineers
KeywordsAnakinraMedicineMyocarditisCytokine stormCardiogenic shockCardiologyInternal medicineHeart failureFulminantMyocardial infarctionInflammationCardiac magnetic resonance imagingEndothelin receptor antagonistImmunologyMagnetic resonance imagingGastroenterologyDiseaseAntagonistCoronavirus disease 2019 (COVID-19)ReceptorRadiologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

A 62-year-old woman with coronavirus disease 2019 developed acute respiratory failure and cardiogenic shock in the setting of a systemic hyperinflammatory state and apparent ST-elevation myocardial infarction. Cardiac magnetic resonance imaging showed fulminant acute myocarditis with severe left ventricular dysfunction. Treatment with the recombinant interleukin-1 receptor antagonist anakinra and dexamethasone resulted in rapid clinical improvement, reduction in serum inflammatory markers, and a marked recovery in cardiac magnetic resonance--based markers of inflammation and contractile dysfunction. The patient was subsequently discharged from the hospital. Emerging evidence supports use of anti-inflammatory therapies, including anakinra and dexamethasone, in severe cases of coronavirus disease 2019.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.115
GPT teacher head0.439
Teacher spread0.324 · 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

Citations27
Published2020
Admission routes1
Has abstractyes

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