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Record W3021616977 · doi:10.1016/j.hrthm.2020.04.039

High-resolution, live, directional mapping

2020· article· en· W3021616977 on OpenAlexaff
D. Curtis Deno, Abhishek Bhaskaran, Dennis J. Morgan, Fikri Goksu, Katherine Batman, Gregory Olson, Karl Magtibay, Sachin Nayyar, Andreu Porta‐Sánchez, Michael A. Laflamme, Stéphane Massé, Prashant Aukhojee, Krishnakumar Nair, Kumaraswamy Nanthakumar

Bibliographic record

VenueHeart Rhythm · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsToronto General Hospital
FundersAbbott Laboratories
KeywordsMedicineCardiac electrophysiologyCardiac arrhythmiaArtificial intelligenceCardiologyComputer scienceInternal medicineElectrophysiologyAtrial fibrillation

Abstract

fetched live from OpenAlex

Conventional arrhythmia mapping involves building activation maps from numerous catheter placements and sequential acquisitions during a stable rhythm. Multielectrode high-density (HD) catheters have enabled comprehensive and dense maps of electrogram (EGM) amplitude and timing. Despite automated algorithms, activation mapping still involves time annotation, numerous acquisitions, and reannotation—often beyond the ability of an electrophysiologist to verify during mapping. Additionally, conventional activation mapping may involve using all or most of the cardiac chamber to locate the source of arrhythmia.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score1.000

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)0.0010.001

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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.

Study designNot applicable
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

Citations69
Published2020
Admission routes1
Has abstractyes

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