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Record W2799557427 · doi:10.1016/j.hrthm.2018.04.021

Cryoballoon Best Practices II: Practical guide to procedural monitoring and dosing during atrial fibrillation ablation from the perspective of experienced users

2018· review· en· W2799557427 on OpenAlexaff
Wilber Su, Arash Aryana, Rod Passman, Gurjit Singh, Robert B. Hokanson, Marcin Kowalski, Jason G. Andrade, Paul J. Wang

Bibliographic record

VenueHeart Rhythm · 2018
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMontreal Heart InstituteUniversity of British Columbia
Fundersnot available
KeywordsMedicineAtrial fibrillationPerspective (graphical)DosingAblationParoxysmal atrial fibrillationIntensive care medicineInternal medicineComputer science

Abstract

fetched live from OpenAlex

Since the evaluation of the cryoballoon in the Sustained Treatment Of Paroxysmal Atrial Fibrillation trial, more than 350,000 patients with atrial fibrillation have been treated. Several studies have reported improved outcomes using the second-generation cryoballoon, and recent publications have evaluated modifications, refinements, and improvements in procedural techniques. Here, peer-reviewed articles published since the first cryoballoon best practices review were summarized against the technical practices of physicians with a high level of experience with the cryoballoon (average ≥6 years of experience in ≥900 cases). This summary includes a comprehensive literature review along with practical usage guidance from physicians using the cryoballoon to facilitate safe, efficient, and effective outcomes for patients with atrial fibrillation.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0110.004

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 head0.149
GPT teacher head0.460
Teacher spread0.311 · 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

Citations86
Published2018
Admission routes1
Has abstractno

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