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Record W4386275802 · doi:10.1177/21501351231186898

Technical Recommendations for Computed Tomography Guidance of Intervention in the Right Ventricular Outflow Tract: Native RVOT, Conduits, and Bioprosthetic Valves

2023· article· en· W4386275802 on OpenAlexaff
Bo Han, Santiago García, Jamil Aboulhosn, Phillip Blanke, Mary Hunt Martin, Evan M. Zahn, Andrew Crean, David M. Overman, Christian Hamilton‐Craig, Kate Hanneman, Thomas Semple, Aimee K. Armstrong

Bibliographic record

VenueWorld Journal for Pediatric and Congenital Heart Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsUniversity of TorontoUniversity of OttawaSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineVentricular outflow tractContext (archaeology)Intervention (counseling)GuidelineRadiologyHeart diseasePercutaneousPulmonary valveCardiac surgeryCardiac imagingCardiologyMedical physicsIntensive care medicinePathology

Abstract

fetched live from OpenAlex

This consensus document for the performance of cardiovascular computed tomography (CCT) to guide intervention in the right ventricular outflow tract (RVOT) in patients with congenital heart disease (CHD) was developed collaboratively by pediatric and adult interventionalists, surgeons, and cardiac imagers with expertise specific to this patient subset. The document summarizes definitions of RVOT dysfunction as assessed by multimodality imaging techniques and reviews existing consensus statements and guideline documents pertaining to indications for intervention. In the context of this background information, recommendations for CCT scan acquisition and a standardized approach for reporting prior to surgical or transcatheter pulmonary valve replacement are proposed and presented. It is the first Imaging for Intervention collaboration for CHD patients and encompasses imaging and reporting recommendations prior to both surgical and percutaneous pulmonary valve replacement.

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)0.001
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 score0.061
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.313
Teacher spread0.284 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

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