Evaluating High-performance Female Athletes’ Knowledge and Awareness of Atrial Fibrillation
Bibliographic record
Abstract
Background: Atrial fibrillation (AFib) is a common cardiac arrhythmia with emerging evidence suggesting a high risk in athletes may exist, especially those engaged in vigorous physical activity. While substantial research has examined male athletes, limited data exists for female athletes, despite their growing presence in elite sports. AFib poses a health risk as it can progress without symptoms, potentially leading to severe complications, including heart failure and stroke. Athletes, accustomed to discomfort in their competitive pursuits, may delay seeking care even after retiring from sport, compounding the issue. Objectives: Therefore, we investigated the level of AFib-related knowledge in Canadian females, defined as high-performance athletes who had or were competing at a national or international level. Methods: Our online cross-sectional survey employed the 21-question Atrial Fibrillation Knowledge and Assessment Tool (AFKAT), with an additional question to assess awareness of an AFib diagnosis. Results: Participants were recruited through research websites and were mostly over 40 years old (45/52), Caucasian (50/52), university educated (39/53), from the sport of curling (41/52), and 62% (32/52) had competed at an international level. The mean knowledge score was 12.63 ± 5.79 out of 21 and was significantly different between international-level athletes (14.22 ± 4.38) when compared to national-level athletes (10.10 ± 6.91; p = 0.011). Conclusions: Varying levels of AFib knowledge were observed within this population, and our results importantly identified a discrepancy based on competition level, with international-level female athletes demonstrating higher AFib knowledge levels. This highlights the necessity for targeted educational interventions, to inform and increase athletes’ cardiovascular awareness of the potential risk of AFib.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".