Establishing modified Canadian Aerobic Fitness Test (mCAFT) cut-points to detect clustered cardiometabolic risk among Canadian children and youth aged 9 to 17 years
Bibliographic record
Abstract
The objective of this study was to establish cut-points to identify potential clustered cardiometabolic risk among children (aged 9–13 years) and youth (aged 14–17 years) using the modified Canadian Aerobic Fitness Test (mCAFT). Nationally representative cross-sectional data were obtained from cycles 1 and 2 (2007–2011) of the Canadian Health Measures Survey. Cardiorespiratory fitness was measured using the mCAFT, which was used to estimate peak oxygen consumption. Clustered cardiometabolic health was identified as the mean of 4 standardized variables: sum of 4 skinfolds; total cholesterol–to–high-density lipoprotein ratio; and systolic and diastolic blood pressure. In total, 2106 (49% female) participants were retained for this analysis. The optimal mCAFT cut-point for males was 49 and 46 mL·kg–1·min–1 among children and youth, respectively. Among females, the mCAFT cut-point was 46 and 37 mL·kg–1·min–1 among children and youth, respectively. In 2016–2017, 83% of females and 71% of males met the new mCAFT cut-points. The mCAFT cut-points can help identify children and youth at potential risk of poor cardiometabolic health in public health surveillance, clinical, and school-based settings. Novelty We developed new mCAFT cut-points to identify potential clustered cardiometabolic risk among Canadian children and youth. These mCAFT cut-points can be used to inform national health surveillance efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".