Role of Physical Activity in the Prevention and Treatment of Type 2 Diabetes
Bibliographic record
Abstract
The prevalence of diabetes in Canada is already high and increasing. It is now estimated that 1.5 million or 5 % of the Canadian population has diagnosed diabetes, a number which is expected to rise to 3 million by the year 2010 (1). This rapid rise in the prevalence of a disease known to have serious health consequences underscores the need to develop effective intervention strategies. This brief review will summarize current knowledge with respect to the utility of physical activity (exercise) as a strategy for the prevention and treatment of type 2 diabetes. Role of physical activity in the prevention of type 2 diabetes. Epidemiological studies using cross-sectional data consistently report that physically active individuals are less likely to develop type 2 diabetes by comparison to sedentary individuals. For example, Mayer-Davis and colleagues reported that participation in both non-vigorous (moderate) and vigorous activity was associated with significantly higher values of insulin action in a cohort of 1467 men and women of African American, Hispanic and White ethnicity that included individuals with type 2 diabetes. Although attenuated, the association between physical activity and insulin action remained significant after controlling for body mass index and waist-hip ratio (2). These findings based on cross-sectional data are strengthened by evidence from prospective cohort studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".