Cardiorespiratory fitness, atrial fibrillation and stroke: a review of the evidence in 2024
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".