Acute glycaemic management before, during and after exercise for cardiac rehabilitation participants with diabetes mellitus: a joint statement of the British and Canadian Associations of Cardiovascular Prevention and Rehabilitation, the International Council for Cardiovascular Prevention and Rehabilitation and the British Association of Sport and Exercise Sciences.
Bibliographic record
Abstract
Type 1 (T1) and type 2 (T2) diabetes mellitus (DM) are significant precursors and comorbidities to cardiovascular disease and prevalence of both types is still rising globally. Currently,~25% of participants (and rising) attending cardiac rehabilitation in Europe, North America and Australia have been reported to have DM (>90% have T2DM). While there is some debate over whether improving glycaemic control in those with heart disease can independently improve future cardiovascular health-related outcomes, for the individual patient whose blood glucose is well controlled, it can aid the exercise programme in being more efficacious. Good glycaemic management not only helps to mitigate the risk of acute glycaemic events during exercising, it also aids in achieving the requisite physiological and psycho-social aims of the exercise component of cardiac rehabilitation (CR). These benefits are strongly associated with effective behaviour change, including increased enjoyment, adherence and self-efficacy. It is known that CR participants with DM have lower uptake and adherence rates compared with those without DM. This expert statement provides CR practitioners with nine recommendations aimed to aid in the participant's improved blood glucose control before, during and after exercise so as to prevent the risk of glycaemic events that could mitigate their beneficial participation.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".