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Record W2137322914 · doi:10.1586/17434440.4.3.307

NaviStar<sup>®</sup>ThermoCool<sup>®</sup>catheter for ventricular tachycardia

2007· review· en· W2137322914 on OpenAlexaff
Amir AbdelWahab, John L. Sapp

Bibliographic record

VenueExpert Review of Medical Devices · 2007
Typereview
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsDalhousie UniversityHealth Sciences Centre
Fundersnot available
KeywordsCatheter ablationAblationVentricular tachycardiaMedicineCardiologyTachycardiaInternal medicineCardiac electrophysiologyAtrial fibrillationRadiofrequency catheter ablationAtrial tachycardiaCatheterRadiofrequency ablationElectrophysiologySurgery

Abstract

fetched live from OpenAlex

Despite the relatively short history of catheter ablation, it has clearly demonstrated high efficacy and safety in treating a wide spectrum of cardiac arrhythmias. An important contributor to this success has been the rapid incorporation of evolving technologies that have changed the practice of electrophysiology remarkably. The introduction of irrigated radiofrequency ablation has allowed electrophysiologists to tackle complex arrhythmias, such as atrial fibrillation and ventricular tachycardia, with a higher success rate. Similarly, the introduction of 3D mapping systems has enhanced our understanding of arrhythmia mechanisms and allowed for integration of electrophysiologic and anatomically guided ablation. In 2006, the US FDA approved the Navistar ThermoCool ablation catheter, which incorporates an irrigated tip design with electroanatomical guidance, for ventricular tachycardia ablation. The design of this catheter, its clinical profile, its potential advantages and possible complications associated with its use in ventricular tachycardia ablation are discussed herein.

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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.004

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 head0.038
GPT teacher head0.402
Teacher spread0.363 · 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

Citations7
Published2007
Admission routes1
Has abstractyes

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