Physical activity for the prevention and management of youth-onset type 2 diabetes mellitus: focus on cardiovascular complications
Bibliographic record
Abstract
With the growing prevalence of childhood obesity and type 2 diabetes mellitus (T2DM) in youth, the challenge of cardiovascular disease risk management has entered the paediatric realm, affecting specialists, family physicians and allied healthcare professionals alike. Currently, there is little evidence to support optimal strategies for management of T2DM in youth and the associated cardiovascular complications. Physical activity plays a powerful role in the prevention and management of T2DM and cardiovascular disease in adults. This review will focus on the role of physical activity for the prevention of T2DM in youth and its associated cardiovascular complications. The first part describes the prevalence of cardiovascular risk factors in this cohort. The second part focuses on the role of physical activity in the prevention and management of T2DM in youth. Collectively, the limited intervention and observation studies published to date suggest that daily targets of 60-90 minutes of physical activity and less than 60 minutes of screen time (i.e. time spent in front of a television, computer or video games) are required for the prevention and management of T2DM in youth. Large-scale intervention studies are needed to determine the most effective physical activity strategies for the prevention and management of T2DM in youth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".