Evidence-based risk assessment and recommendations for physical activity clearance: diabetes mellitus and related comorbidities<sup>1</sup>This paper is one of a selection of papers published in this Special Issue, entitled Evidence-based risk assessment and recommendations for physical activity clearance, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Physical activity (PA) is one of the most powerful treatment options for persons with prediabetes or diabetes. However, some elevation in risk occurs with increased PA, at least initially, and certain precautions need to be made to lower these risks, particularly if these persons are unaccustomed to exercise. We conducted a standardized search of all adverse events associated with increased PA in persons with prediabetes or diabetes (type 1 or type 2) and provided evidence-based guidelines on PA screening in these apparently high-risk individuals. A systematic literature review was performed of all studies reporting on adverse events in persons with prediabetes or diabetes. Studies included were from all designs (retrospective and prospective including randomized controlled trials) and were assessed according to evaluation criteria adapted by a consensus panel. A total of 47 studies, involving >8000 individuals, were deemed eligible. A number of these studies identified a range of mild to severe acute risks with exercise (musculoskeletal injury, hypoglycemia, foot ulceration, proliferative retinopathy, hypotension, sudden death) but the overall prevalence was low. Based on several randomized controlled trials and prospective studies in which prescribed exercise was performed at a wide range of intensities, it appears that increased PA is a relatively safe procedure with no evidence of a loss of life. Based on our assessment of the available literature, we provide a new PA risk algorithm for persons with prediabetes and diabetes and comment on the role of the patient, the qualified exercise professional, and the patient's physician in the risk screening process.
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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 | 0.071 | 0.312 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".