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Record W2153973795 · doi:10.1586/14779072.2.1.117

Radiofrequency applications in congenital heart disease

2004· review· en· W2153973795 on OpenAlexaff
Gruschen Veldtman, Amanda Hartley, Naheed Visram, Lee Benson

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2004
Typereview
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineCardiologyPercutaneousRadiofrequency ablationInternal medicinePulmonary atresiaAtrial fibrillationVentricular outflow tractCatheterHeart diseaseSurgeryAblation

Abstract

fetched live from OpenAlex

The relatively recent application of radiofrequency technologies in the treatment of congenital heart defects has provided a safe and effective alternative to conventional therapies in establishing endovascular patency for a variety of lesions. Radiofrequency, with typically used frequencies of approximately 500 kHz, does not cause pain and is unlikely to induce atrial or ventricular fibrillation. It can be used either to ablate (higher power (35-50 W); longer duration of application (90-120 sec); lower voltage (30-50 V)) or to perforate (lower power (5-10 W) shorter duration of application (1-5 sec), higher voltage (150-280 V)). In the past, perforating radiofrequency has been applied to establish right ventricular outflow tract patency in pulmonary atresia with intact septum and with ventricular septal defect. More recently radiofrequency has been shown to be effective at recanalizing central and peripheral vasculature and has also been applied in establishing percutaneous left heart access. A new radiofrequency catheter, dedicated to transseptal left atrial cannulation, has been demonstrated to be safe and effective in an animal model and is now ready for clinical trials.

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)0.001
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.362
Teacher spread0.326 · 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

Citations17
Published2004
Admission routes1
Has abstractyes

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