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Record W4413031073 · doi:10.1016/j.jaccas.2025.104927

Desmoplakin Cardiomyopathy Presenting as Recurrent Myocarditis Treated With Immunosuppression

2025· article· en· W4413031073 on OpenAlexaff
Jacob Abdaem, Nathan Leader, Brennan Ballantyne, Jeffrey P. Shaw, James A. White, Omid Kiamanesh

Bibliographic record

VenueJACC Case Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicPericarditis and Cardiac Tamponade
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDesmoplakinImmunosuppressionMyocarditisCardiomyopathyMedicineCardiologyInternal medicineHeart failureBiologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Desmoplakin (DSP) cardiomyopathy is a genetic cardiomyopathy which causes myocarditis, heart failure, and sudden death. CASE SUMMARY: A 42-year-old female presented with palmoplantar keratoderma, recurrent magnetic resonance imaging-confirmed myocarditis, and intense inflammation on cardiac positron emission tomography. Genetic testing confirmed DSP cardiomyopathy with a novel heterozygous DSP c.123C>G (p.Tyr41∗) truncating variant. Immunosuppression with prednisone and mycophenolate mofetil produced marked improvement on repeat positron emission tomography imaging. Predictive testing identified the same mutation in her 44-year-old sister. Cardiac magnetic resonance imaging revealed occult cardiomyopathy with severe myocardial fibrosis of similar phenotype. Both patients received heart failure therapies and were offered primary prevention implantable cardioverter defibrillator. DISCUSSION: A novel DSP truncating variant associated with inflammatory cardiomyopathy was identified. We propose a treatment algorithm to manage DSP cardiomyopathy incorporating predictive testing of relatives, immunosuppression of myocardial inflammation, risk stratification of sudden death, and initiation of heart failure therapies. TAKE-HOME MESSAGE: We present a novel DSP variant and algorithm to manage DSP cardiomyopathy.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.273
Teacher spread0.266 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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