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Record W4395050618 · doi:10.1097/hco.0000000000001154

Recent advances in multimodal imaging in tetralogy of fallot and double outlet right ventricle

2024· article· en· W4395050618 on OpenAlexaff
Aakansha Ajay Vir Singh, Shi‐Joon Yoo, Mike Seed, Christopher Z. Lam, Israel Valverde

Bibliographic record

VenueCurrent Opinion in Cardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineTetralogy of FallotDouble outlet right ventricleVentricleCardiologyInternal medicineRadiologyHeart disease

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: In the ever-evolving field of medical imaging, this review highlights significant advancements in preoperative and postoperative imaging for Tetralogy of Fallot (TOF) and double outlet right ventricle (DORV) over the past 18 months. RECENT FINDINGS: This review showcases innovations in echocardiography such as 3D speckle tracking echocardiography (3DSTE) for assessing right ventricle-pulmonary artery coupling (RVPAC) and Doppler velocity reconstruction (DoVeR) for intracardiac flow fields evaluation. Furthermore, advances in assessment of cardiovascular anatomy using computed tomography (CT) improve the integration of imaging in ablation procedures. Additionally, the inclusion of cardiac magnetic resonance (CMR) parameters as risk score predictors for morbidity, and mortality and for timing of pulmonary valve replacement (PVR) indicates its significance in clinical management. The utilization of 4D flow techniques for postoperative hemodynamic assessment promises new insights into pressure mapping. Lastly, emerging technologies such as 3D printing and 3D virtual reality are expected to improve image quality and surgical confidence in preoperative planning. SUMMARY: Developments in multimodality imaging in TOF and DORV are poised to shape the future of clinical practice in this field.

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 score0.441
Threshold uncertainty score0.407

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)0.0000.000

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 head0.036
GPT teacher head0.370
Teacher spread0.334 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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