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Record W4403454778 · doi:10.1016/j.jacadv.2024.101329

Intracardiac Echocardiography to Assist Anatomical Isthmus Ablation in Repaired Tetralogy of Fallot Patients With Ventricular Tachycardia

2024· article· en· W4403454778 on OpenAlexaff
Nathan Denham, Raja Selvaraj, Jayant Kakarla, Sirish Chandra Srinath Patloori, S. Lucy Roche, Sara Thorne, Erwin Oechslin, Danielle Massarella, Rachel M. Wald, Rafael Alonso-González, Candice Silversides, Eugene Downar, Krishnakumar Nair

Bibliographic record

VenueJACC Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsIntracardiac injectionTetralogy of FallotCardiologyInternal medicineMedicineVentricular tachycardiaAblationTachycardiaHeart disease

Abstract

fetched live from OpenAlex

Background: Successful catheter ablation of ventricular tachycardia (VT) in repaired tetralogy of Fallot (TOF) can be achieved by targeting 1 or more anatomical isthmuses. However, significant interindividual variability in the size and location of surgical patches means careful mapping is required to design ablation lines to block the isthmus. Intracardiac echocardiography (ICE) may assist ablation by accurate identification of individual TOF anatomy. Objectives: The authors hypothesized ICE-guided VT ablation improved isthmus recognition, ablation, and procedural outcomes. Methods: Retrospective study of adults with repaired TOF undergoing VT ablation between January 1, 2017 and December 31, 2022. ICE integration was compared to a strategy using electroanatomical mapping only to identify anatomic boundaries. All cases underwent ablation and had proven isthmus block as the procedural endpoint. Results: = 1.000). Conclusions: ICE improves ablation of the anatomical isthmus for sustaining VT in patients with repaired TOF by demonstrating the individual anatomy but does not improve long-term outcomes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.250
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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