Community-Based Exercise Assessment in Children With High Risk for Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To assess the risk factors for a group of children at high risk for type 2 diabetes using a proven medical model and incorporating a community-friendly standardized fitness assessment tool. DESIGN: All children in school (kindergarten to grade eight) in an isolated First Nation community were enrolled to be screened for diabetes, risk factors, and fitness level. SETTING: Beausoleil First Nation community is an Ojibway community situated on Christian Island in Georgian Bay, Ontario, Canada. PARTICIPANTS: All children attending Christian Island Elementary School, a total of 101 students from kindergarten to grade eight. INTERVENTIONS: Capillary blood glucose fasting and 2 hours after 75-g simple carbohydrate meal, height and weight (calculated BMI), blood pressure, aerobic capacity, abdominal strength and endurance, upper body strength, trunk extensor strength and flexibility, and upper body flexibility. OUTCOME MEASUREMENTS: Applicability of tests to assess disease, risk factors, and fitness level. RESULTS: Eight children were found to have abnormal capillary blood glucose and required further laboratory investigations. Significant risk factors for type 2 diabetes were identified. The screening exercise assessment identified specific areas below that considered a healthy fitness zone. CONCLUSIONS: The screening assessment identified medical areas of concern in capillary blood glucose, blood pressure, and body mass index. The fitness testing identified areas of concern in aerobic capacity, upper body strength, abdominal strength and endurance, and flexibility. The fitness testing was First Nation community-friendly.
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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 | 0.002 |
| 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".