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Record W2980261676 · doi:10.14740/jnr.v9i4-5.553

Ischemic Stroke Secondary to Left Ventricular Noncompaction

2019· article· en· W2980261676 on OpenAlexvenueno aff
Hussam A. Yacoub, Keithan Sivakumar, Mohammed El‐Hunjul, Chun Chu, Dev Mehta

Bibliographic record

VenueJournal of Neurology Research · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLeft ventricular noncompactionCardiologyInternal medicineVentricleStroke (engine)Dilated cardiomyopathyCardiomyopathyEmbolismMagnetic resonance imagingIschemic strokeCardiac magnetic resonance imagingRadiologyHeart failureIschemia

Abstract

fetched live from OpenAlex

Left ventricular noncompaction (LVNC) is a rare cause of cardiomyopathy that can lead to systemic embolism and ischemic stroke. LVNC results from the arrest of ventricular myocardium compaction during embryogenesis. Multiple other cardiac complications coexist with this cardiomyopathy, and one of the potential consequences is cardioembolic events causing ischemic stroke. This condition can be underdiagnosed or misdiagnosed as hypertrophic or dilated cardiomyopathy. We report a patient who presented with recurrent embolic ischemic stroke secondary to LVNC that was overlooked on initial transthoracic echocardiographic studies. After an unremarkable laboratory workup, transesophageal echocardiogram (TEE) revealed noncompaction of the myocardium within the apex of the left ventricle, confirmed by cardiac magnetic resonance imaging (MRI). The patient was anticoagulated with warfarin and discharged to a rehabilitation facility. Increased understanding and awareness of the diagnosis, clinical manifestations, and management of LVNC are advised, particularly in patients with recurrent embolic stroke of undetermined source. Screening of all first-degree relatives with this familial condition is recommended as well, as appropriate treatment can prevent cardiac complications and sudden death. J Neurol Res. 2019;9(4-5):75-80 doi: https://doi.org/10.14740/jnr553

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.371
Teacher spread0.331 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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