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Record W4402928651 · doi:10.1080/14779072.2024.2409440

Cardiorespiratory fitness, atrial fibrillation and stroke: a review of the evidence in 2024

2024· review· en· W4402928651 on OpenAlexaff
Setor K. Kunutsor, Sudhir Kurl, Jari A. Laukkanen

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2024
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsMedicineCardiorespiratory fitnessAtrial fibrillationStroke (engine)CardiologyInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The body of evidence linking cardiorespiratory fitness (CRF) levels with the risk of atrial fibrillation (AF) and stroke - two interconnected cardiovascular conditions - is not entirely consistent. Furthermore, specific CRF thresholds beyond which the risk of AF or stroke might not decrease are not well defined. AREAS COVERED: This review summarizes research evidence on the role of CRF in the development of AF and stroke including dose-response relationships in general population participants, explores the biological mechanisms through which CRF may exert its effects, assesses the potential implications for clinical care and population health, identifies gaps in the current evidence, and suggest directions for future research. MEDLINE and Embase were searched from inception until July 2024 to identify observational longitudinal and interventional studies as well as systematic reviews and meta-analyses related to these study designs. EXPERT OPINION: In the general population, increasing levels of CRF, achieved through consistent physical activity, can significantly reduce the likelihood of developing AF and stroke. The findings also advocate for a tailored approach to exercise prescriptions, acknowledging the plateau in benefits for AF risk beyond certain CRF levels, while advocating for higher intensity or prolonged activity to further reduce stroke risk.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.011
Bibliometrics0.0000.001
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 head0.118
GPT teacher head0.410
Teacher spread0.292 · 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 designSystematic review
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
Published2024
Admission routes1
Has abstractyes

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