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Record W4391348404 · doi:10.3390/life14020200

Novel Magnetic Resonance Imaging Tools for Hypertrophic Cardiomyopathy Risk Stratification

2024· article· en· W4391348404 on OpenAlexaff
Fahad Alajmi, Mehima Kang, James Dundas, Alexander Haenel, Jeremy Parker, Philipp Blanke, Fionn Coghlan, John Khoo, Abdulaziz A. Bin Zaid, Amrit Singh, Bobby Heydari, Darwin F. Yeung, Thomas M. Roston, Kevin Ong, Jonathon Leipsic, Zachary Laksman

Bibliographic record

VenueLife · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRisk stratificationHypertrophic cardiomyopathyMagnetic resonance imagingSudden cardiac deathMedicineCardiologyCardiac magnetic resonanceInternal medicineSudden deathCardiac magnetic resonance imagingCardiomyopathyRisk assessmentRadiologyComputer scienceHeart failureComputer security

Abstract

fetched live from OpenAlex

Hypertrophic cardiomyopathy (HCM) is a common genetic disorder with a well described risk of sudden cardiac death; however, risk stratification has remained a challenge. Recently, novel parameters in cardiac magnetic resonance imaging (CMR) have shown promise in helping to improve upon current risk stratification paradigms. In this manuscript, we have reviewed novel CMR risk markers and their utility in HCM. The results of the review showed that T1, extracellular volume, CMR feature tracking, and other miscellaneous novel CMR variables have the potential to improve sudden death risk stratification and may have additional roles in diagnosis and prognosis. The strengths and weaknesses of these imaging techniques, and their potential utility and implementation in HCM risk stratification are discussed.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.274
Teacher spread0.246 · 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 designObservational
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
Published2024
Admission routes1
Has abstractyes

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