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Record W4414937345 · doi:10.1161/res.137.suppl_1.or302

Abstract Or302: Differentiating Local from Global Repolarization Changes During Intra-Cardiac Mapping: The Inception of a Novel Mapping Technology

2025· article· en· W4414937345 on OpenAlexaff
Tasnia Subha, Stéphane Massé, Yusuf Abderrahman, Patrick F.H. Lai, John Asta, Abhishek Bhaskaran, Praloy Chakraborty, Vijay S. Chauhan, Paul Dorian, K. Nanthakumar

Bibliographic record

VenueCirculation Research · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsSt. Michael's HospitalUniversity Health Network
Fundersnot available
KeywordsOptical mappingRepolarizationLidocaineLocal field potentialOptical imaging

Abstract

fetched live from OpenAlex

Background: Steep local repol gradients are critically more important than global repol changes. Activation recovery intervals (ARI) of unipolar electrograms (Uni) estimation of repol integrate far-field signals and may not detect local gradients. Multielectrode array catheters have allowed for the innovation of principal component-referenced Uni (Uni PCR ), which attenuates far-field contribution in electrical signals. Here we apply this concept to estimate global and local repol changes. Hypothesis: We assessed the hypothesis that Uni PCR ARI will correlate to optical APD80 better than Uni ARI and Uni PCR ARI will more accurately detect local repol changes than Uni ARI by attenuating the far-field. Aim: We aimed to validate Uni PCR as a superior alternative to the Wyatt ARI method on Uni with optical APD80 as reference in detecting local repol changes. Methods: To validate Uni PCR ARI for detecting global repol changes, we conducted simultaneous optical and electrical mapping in a rabbit model (n=3) following pinacidil/ibutilide infusion and compared optical APD to Uni vs Uni PCR ARIs. To alter repol locally, we performed epicardial mapping in pig Langendorff experiments (n=4) and topically administered lidocaine at the center of the electrode array. We compared the changes in ARI from Uni vs. Uni PCR before and after application of the drug. Results: When repol is altered globally, there is a high correlation between both Uni PCR and optical APD80 (slope = 0.98, R 2 = 0.90) and Uni and optical APD80 (slope = 0.99, R 2 = 0.91). When there is a local repol gradient, a greater difference in percent change near vs. far from the application site was observed in the Uni PCR (32.0%, p <0.0001) compared to the Uni (5.2%, p = 0.0144). Uni PCR ARI (AUC-ROC = 0.9) more accurately predicted areas near vs. far from the application site than Uni ARI (AUC-ROC = 0.7). Conclusion: Global changes in repol can be detected by both Uni and Uni PCR ARI. However, Uni PCR is more sensitive to local repol gradients than Uni ARI. Implementing such mapping technology has greater implications for defining arrhythmogenicity of regional myocardium.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.095
GPT teacher head0.393
Teacher spread0.298 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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