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Record W2969211651 · doi:10.1111/jocs.14194

Atrial fibrillation: Current and emerging surgical strategies

2019· review· en· W2969211651 on OpenAlexaff
Ali Fatehi Hassanabad, Hallie L. Jefferson, Ganesh Shanmugam, William Kent

Bibliographic record

VenueJournal of Cardiac Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineAtrial fibrillationCatheter ablationIntensive care medicineManagement of atrial fibrillationSurgical proceduresReview articleStroke (engine)SurgeryGeneral surgeryCardiologyPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: (a) To provide a comprehensive review of current literature on the surgical management of atrial fibrillation (AF), highlighting surgical approaches and outcomes. (b) To summarize the latest guidelines pertinent to the surgical management of AF. BACKGROUND: AF is associated with high rates of morbidity and mortality, primarily related to the associated risk of stroke. The mainstay of management is pharmacologic rate or rhythm control and catheter-based ablation. Surgical ablation (SA) is an alternative strategy that is effective in select patient populations. Recently, novel techniques and technologies have been introduced and this has expanded the surgical capacity to manage AF. METHODS: A comprehensive review of the literature was conducted. RESULTS: Surgery can be a highly effective alternative therapeutic option for the management of AF in the appropriate patient population. The need for permanent pacemaker implantation is controversial among patients undergoing surgical intervention for AF. Surgical outcomes are promising, with long-term control of AF and symptomatic relief achieved in select groups of patients. CONCLUSIONS: This article provides a comprehensive review of the surgical management of AF. We have summarized the latest surgical outcomes and contextualized the most recent guidelines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.984
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.139
GPT teacher head0.413
Teacher spread0.274 · 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 teacher head, 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

Citations4
Published2019
Admission routes1
Has abstractyes

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