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Record W4225626385 · doi:10.15420/ecr.2021.38

Cryoablation or Drug Therapy for Initial Treatment of Atrial Fibrillation

2022· review· en· W4225626385 on OpenAlexaff
Jason G. Andrade, Ricky D. Turgeon, Laurent Macle, Marc W. Deyell

Bibliographic record

VenueEuropean Cardiology Review · 2022
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversité de MontréalMontreal Heart InstituteBC Innovation CouncilUniversity of British Columbia
FundersBiosense WebsterServierBoston Scientific CorporationPfizer
KeywordsCryoablationMedicineAtrial fibrillationSinus rhythmCatheter ablationGuidelineAblationQuality of life (healthcare)Internal medicineDigoxinCardiologyIntensive care medicineHeart failure

Abstract

fetched live from OpenAlex

AF is a common chronic and progressive disorder. Without treatment, AF will recur in up to 75% of patients within a year of their index diagnosis. Antiarrhythmic drugs (AADs) have been proven to be more effective than placebo at maintaining sinus rhythm and remain the recommended initial therapeutic option for AF. However, the emergence of 'single-shot' AF ablation toolsets, which have enabled enhanced procedural standardisation and consistent outcomes with low rates of complications, has led to renewed interest in determining whether first-line catheter ablation may improve outcomes. The recently published EARLY-AF trial evaluated the role of initial cryoballoon ablation versus guideline-directed AAD therapy. Compared to AADs, an initial treatment cryoballoon ablation strategy resulted in greater freedom from atrial tachyarrhythmia, superior reduction in AF burden, greater improvement in quality of life and lower healthcare resource utilisation. These findings are relevant to patients, providers and healthcare systems when considering the initial treatment choice for rhythm-control therapy.

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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.299
GPT teacher head0.453
Teacher spread0.154 · 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

Citations33
Published2022
Admission routes1
Has abstractyes

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