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Record W1973432408 · doi:10.5402/2012/606324

Minimally Invasive Surgical Therapies for Atrial Fibrillation

2012· article· en· W1973432408 on OpenAlexaff
Yoshitsugu Nakamura, Bob Kiaii, Michael Chu

Bibliographic record

VenueISRN Cardiology · 2012
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsLondon Health Sciences CentreLawson Health Research InstituteWestern University
Fundersnot available
KeywordsAtrial fibrillationMedicineSinus rhythmCardiologyAblationHeart failureInternal medicineStroke (engine)Pulmonary veinAntithromboticSurgery

Abstract

fetched live from OpenAlex

Atrial fibrillation is the most common sustained arrhythmia and is associated with significant risks of thromboembolism, stroke, congestive heart failure, and death. There have been major advances in the management of atrial fibrillation including pharmacologic therapies, antithrombotic therapies, and ablation techniques. Surgery for atrial fibrillation, including both concomitant and stand-alone interventions, is an effective therapy to restore sinus rhythm. Minimally invasive surgical ablation is an emerging field that aims for the superior results of the traditional Cox-Maze procedure through a less invasive operation with lower morbidity, quicker recovery, and improved patient satisfaction. These novel techniques utilize endoscopic or minithoracotomy approaches with various energy sources to achieve electrical isolation of the pulmonary veins in addition to other ablation lines. We review advancements in minimally invasive techniques for atrial fibrillation surgery, including management of the left atrial appendage.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.073
GPT teacher head0.348
Teacher spread0.276 · 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 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

Citations9
Published2012
Admission routes1
Has abstractyes

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