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Record W2517897451 · doi:10.5430/jbei.v3n1p1

3D phase diagram in vectorcardiography

2016· article· en· W2517897451 on OpenAlexaff
Valeri Goussev

Bibliographic record

VenueJournal of Biomedical Engineering and Informatics · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsJewish Rehabilitation Hospital
Fundersnot available
KeywordsVectorcardiographyPhase diagramDiagramDipolePhase (matter)Computer sciencePhysicsAlgorithmMathematicsCardiologyMedicineStatisticsElectrocardiography

Abstract

fetched live from OpenAlex

The article is intended to propose the new technique for analysis and visualization of vectorcardiograms based on the 3D phase diagrams. The regular Frank 3D lead system was considered as the signal source to construct 3D vector space. The three cardio signals from the lead system, representing the currents in the body, and the three integrated in time signals, representing the corresponding charge flows, were used to form 3D phase diagram. This diagram is considered as a new compact description of the dipole object properties in the 3D space, combining simultaneously information about the charge movements and the changes in values and orientation of the current. The regular properties, like the angular momentum of the charge flow and the dipole strength vectors and their covariance can be evaluated from the real vectorcardiogram. Based on the set of vectorcardiograms for 8 healthy controls and 7 myocardial infarction patients the 3D phase diagrams and their statistical parameters are evaluated and discussed. An example is given of the technique implementation for the comparison of the 3D phase diagrams in a control and a myocardial infarction patient.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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 designOther design
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

Citations2
Published2016
Admission routes1
Has abstractyes

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