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Record W3103439105 · doi:10.1016/j.cjco.2020.11.004

Plasma Exchange for Immune Checkpoint Inhibitor–Induced Myocarditis

2020· article· en· W3103439105 on OpenAlexaff
Haran Yogasundaram, Waleed Alhumaid, June W. Chen, Matthew Church, Naji Alhulaimi, Shane Kimber, D. Ian Paterson, Janek Senaratne

Bibliographic record

VenueCJC Open · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPembrolizumabFulminantCardiogenic shockMyocarditisMedicineTherapeutic plasma exchangeImmunosuppressionAdverse effectRefractory (planetary science)NivolumabInternal medicineImmune systemImmunologyImmunotherapyMyocardial infarction

Abstract

fetched live from OpenAlex

Immune checkpoint inhibitor therapy has been shown to improve outcomes across many types of malignancies. However, immune checkpoint inhibitor has been associated with several immune-related adverse events including myocarditis. We describe the case of a 69-year-old man with fulminant myocarditis likely due to pembrolizumab therapy, complicated by biventricular failure with cardiogenic shock. Because of deterioration in hemodynamic status refractory to conventional immunosuppression, therapeutic plasma exchange was performed, resulting in a rapid reduction of serum pembrolizumab levels, and marked clinical, radiological, and biochemical improvement. To our knowledge, this is the first reported case on the successful use of plasma exchange for pembrolizumab-associated fulminant myocarditis.

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)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.056
GPT teacher head0.312
Teacher spread0.257 · 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

Citations36
Published2020
Admission routes1
Has abstractyes

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