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Record W2594022493 · doi:10.22489/cinc.2016.092-434

Noninvasive Epicardial and Endocardial Electrocardiographic Imaging of Scar:Related Ventricular Tachycardia

2016· article· en· W2594022493 on OpenAlexaff
Linwei Wang, Omar Gharbia, Sandesh Ghimire, B. Milan Horáček, John L. Sapp

Bibliographic record

VenueComputing in cardiology · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCardiologyInternal medicineVentricular tachycardiaMedicineElectrocardiography

Abstract

fetched live from OpenAlex

An effective treatment for scar-related ventricular tachycardia (VT) is to interrupt the circuit by catheter ablation.If activation sequence and entrainment mapping can be performed during sustained VT, the exit and isthmus of the circuit can often be identified.However, with invasive catheter mapping, only monomorphic VT that is hemodynamically stable can be mapped in this manner.A noninvasive approach to fast mapping of unstable VTs can potentially allow an improved identification of critical ablation sites.In this pilot study, noninvasive ECG-imaging were carried out on patients with unstable scar-related VT.The reconstructed reentry circuits correctly revealed both epicardial and endocardial origins of activation, consistent with locations of exit sites found during ablation procedures.The results also indicated that some reentry circuits involve both epicardial and endocardial layers, and can only be properly interpreted by mapping both layers.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.238
Teacher spread0.233 · 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
GenreMethods

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
Published2016
Admission routes1
Has abstractyes

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