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Record W2969180543 · doi:10.1016/j.ccep.2019.05.003

Reinserting Physiology into Cardiac Mapping Using Omnipolar Electrograms

2019· review· en· W2969180543 on OpenAlexaff
Karl Magtibay, Andreu Porta‐Sánchez, Shouvik Haldar, D. Curtis Deno, Stéphane Massé, Kumaraswamy Nanthakumar

Bibliographic record

VenueCardiac Electrophysiology Clinics · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsToronto General HospitalUniversity Health Network
FundersAbbott Laboratories
KeywordsWavefrontOrientation (vector space)MedicineOptical mappingCardiac electrophysiologyElectrode arrayBeat (acoustics)ElectrophysiologyVoltageBiomedical engineeringNeuroscienceCardiologyAcousticsInternal medicineElectrical engineeringPhysicsOpticsMathematics

Abstract

fetched live from OpenAlex

Omnipolar electrograms (EGMs) make use of biophysical electric fields that accompany activation along the surface of the myocardium. A grid-like electrode array provides bipolar signals in orthogonal directions to deliver catheter-orientation-independent assessments of cardiac electrophysiology. Studies with myocyte monolayers, isolated animal and human hearts, and anesthetized animals validated the tenets of omnipolar EGMs. The combination of information from omnipolar-based activation vectors and voltages may aid in localizing areas of scar, lesion gaps, wavefront disorganization, and fractionation or collision during arrhythmias. The goal of omnipolar EGMs is to better characterize myocardium through reintroducing electrogram direction related fundamentals of cardiac electrophysiology.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.069
GPT teacher head0.387
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2019
Admission routes1
Has abstractno

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