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Record W4288073552 · doi:10.1097/crd.0000000000000373

Eosinophilic Myocarditis: When Allergies Attack the Heart!

2021· article· en· W4288073552 on OpenAlex
Vardhmaan Jain, Agam Bansal, Devika Aggarwal, Michael Chetrit, Manasvi Gupta, Kirtipal Bhatia, Samarthkumar Thakkar, Rajkumar Doshi, Raktim K. Ghosh, Dhrubajyoti Bandopadhyay, Benico Barzilai, Carolyn J Shiau, William H. Frishman, Wilbert S. Aronow

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

VenueCardiology in Review · 2021
Typearticle
Languageen
FieldMedicine
TopicEosinophilic Disorders and Syndromes
Canadian institutionsRoyal Columbian Hospital
Fundersnot available
KeywordsMedicineMyocarditisEosinophiliaHypereosinophilic syndromeFulminantSubclinical infectionImmunosuppressionEosinophilicMyeloidPathologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Eosinophilic myocarditis is a clinical condition whereby myocardial injury is mediated by eosinophilic infiltration. A number of underlying causes, including reactive, clonal, or idiopathic hypereosinophilic syndrome, may trigger eosinophilia. Disease presentation may vary from mild subclinical variants to fulminant myocarditis with thromboembolic complications, and in some cases, endomyocardial and valvular fibrosis may be seen. A detailed examination coupled with the use of multimodality imaging, and endomyocardial biopsy may help establish diagnosis. Treatment is aimed at symptomatic management and treating the underlying cause of eosinophilia, such as withdrawal of implicated drugs, antihelminthic therapy for infection, immunosuppression for autoimmune conditions, and targeted therapy with tyrosine kinase inhibitors in cases with clonal myeloid disorders.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.189
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.323
Teacher spread0.280 · 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