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Record W3012661133 · doi:10.4414/smw.2020.20196

Atrial fibrillation for internists: current practice

2020· review· en· W3012661133 on OpenAlexaff
Pascal Meyre, David Conen, Stefan Osswald

Bibliographic record

VenueSwiss Medical Weekly · 2020
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)Intensive care medicineContext (archaeology)Adverse effectPsychological interventionInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Atrial fibrillation (AF) has become a global epidemic and puts affected patients at high risk of adverse events. In this review we summarise the current evidence on risk factors and complications of AF, describe current treatment strategies, and outline new fields of research. Current evidence shows that hypertension and obesity are the two most important modifiable risk factors for the development of AF. Patients with AF face an increased stroke risk. Oral anticoagulation reduces this risk substantially. Mainly for reasons of safety and ease of use, non-vitamin K antagonist oral anticoagulants are preferred for stroke prevention. Rate and rhythm control interventions remain important and are mainly used for symptom control in AF patients. Rate control is recommended as an initial treatment and in patients with a low or absent symptom burden. Following the advent of AF ablation 20 years ago, the chances of successful sustained rhythm control have increased. Nevertheless, the procedural risks, although low, must be discussed with the patient in the context of the potential benefits. Heart failure and AF often coexist, which creates a further challenge for optimal AF management. Recent studies have shown that AF patients have a high burden of silent brain lesions, and that these lesions are associated with cognitive dysfunction. A better understanding of these interrelationships may eventually help the development of new prevention and treatment strategies to decrease the burden and complications associated with AF.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.167
GPT teacher head0.481
Teacher spread0.314 · 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.

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

Citations5
Published2020
Admission routes1
Has abstractyes

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