Characterization of aerobically fit individuals with cardiovascular disease
Bibliographic record
Abstract
With an ageing population there is an increased prevalence of individuals living with cardiovascular disease (CVD). Characteristics of older aerobically fit individuals with previously diagnosed CVD have not been studied. Therefore, our knowledge is limited as to how, or if, aerobically fit individuals with CVD attempt to adapt their physical activity and the intensity of their training programmes. The objective of this paper is to characterise the physical activity habits and behaviours of older aerobically fit individuals with CVD. We identified 28 aerobically fit patients with CVD from those who completed a minimum of 15 and 12 min of the Bruce treadmill protocol for men and women, respectively. Consenting participants responded to questionnaires regarding physical activity levels, competitive event participation and self-monitoring since diagnosis of heart disease. Average age and treadmill time of participants were 56 and 49 years and 15.6 and 13.0 min for males and females, respectively. Data were obtained regarding recent medical history (medical diagnoses, surgeries/procedures). Despite the majority of individuals participating in the same or more activity since their diagnosis, 25% indicated that their condition limited their activity and 39% reported having symptoms during activity. Nearly all participants (93%) indicated that they monitored their heart rate during exercise. However, only 14% of participants stated that their physician advised them on how to exercise safely. It is necessary for physicians and cardiac rehabilitation programmes to be involved in safe and effective exercise programming to allow individuals to return to sport after CVD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".