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Record W2155956461 · doi:10.1109/cic.1994.470212

Three dimensional analysis of cardiac shape and load using tagged magnetic resonance imaging

2002· article· en· W2155956461 on OpenAlexaff
Yael Petrank, Sheng Jing Dong, John V. Tyberg, S. Sideman, R. Beyar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMagnetic resonance imagingHypertrophic cardiomyopathyNuclear magnetic resonanceCardiac magnetic resonanceFlatteningAnatomyMedicineCardiologyPhysicsRadiology

Abstract

fetched live from OpenAlex

Three dimensional (3D) reconstructions of the heart from sets of two dimensional (2D) tagged magnetic resonance imaging (MRI) cross sections, were used to characterize the 3D geometry of the left ventricles in patients with hypertrophic cardiomyopathy (HCM) and patients with right ventricular pressure overload (RVPO). Curvatures, stresses, wall thickness and endocardial motion were calculated from surface and volume elements. Hearts with RVPO exhibited more circumferential and meridional flattening of the septum than normal and HCM hearts. The stress indices were lowest in the HCM hearts, compared to normal and RVPO hearts, due to the larger thicknesses. There was a more significant difference between lateral wall motion, and other regional wall motions in the HCM and RVPO hearts as compared to normal hearts. It is suggested that curvature and stress mapping by 3D tagged MRI can be advantageously used as an important clinical tool.>

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.235
Teacher spread0.218 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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