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Record W4383873833 · doi:10.1111/pace.14778

Epicardial connections and bi‐atrial tachycardias: From anatomy to clinical practice

2023· review· en· W4383873833 on OpenAlexaff
Yiwei Lai, Weili Ge, Caihua Sang, Laurent Macle, Ribo Tang, Deyong Long, Jianzeng Dong, Changsheng Ma

Bibliographic record

VenuePacing and Clinical Electrophysiology · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineCardiologyClinical PracticeInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Bi-atrial tachycardia (BiAT) is not rare after extensive atrial ablation or cardiac surgery. The complexity of bi-atrial reentrant circuits poses a great challenge for clinical practice. With recent advances in mapping technologies, we are now able to characterize atrial activation in detail. However, given the involvement of both atria and multiple epicardial conductions, endocardial mapping for BiATs is not easy to understand. Knowledge of the atrial myocardial architecture is the foundation for the clinical management of BiATs; as it is required to understand the possible mechanism of the tachycardia and identify the optimal target of ablation. In this review we summarize current knowledge about the anatomy of interatrial connections as well as other epicardial fibers and discuss the interpretation of electrophysiological findings and ablation strategies for BiATs.

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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.087
GPT teacher head0.497
Teacher spread0.410 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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