Investigation of Underlying Association between Anthropometric and Cardiorespiratory Fitness Markers among Overweight and Obese Adolescents in Canada
Bibliographic record
Abstract
BACKGROUND: Adolescents who experience overweight or obesity commonly persist in these conditions into adulthood, thereby elevating their vulnerability to health issues. The focus of this study is on health risk markers such as body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), body surface area (BSA), and cardiorespiratory fitness (CRF). The objectives include updating normative values for BMI, WC, WHtR, and BSA in Canadian adolescents, establishing cardiometabolic risk zones, and developing a composite score considering both anthropometric and CRF markers. METHODS: Involving 1864 adolescents, the study used the LMS method to generate percentile norms, stratified by age and sex. Cardiometabolic risk zones were established for each marker based on Z-scores, and a composite score was created. RESULTS: = 0.890) of the common variance between the composite score and the retained independent variables. Moreover, the model demonstrated a mean absolute error (MAE) of approximately 6 percentiles, confirming its high precision. Furthermore, these analyses yielded key thresholds for identifying adolescents at risk: the 70th percentile for high cardiometabolic risk and the 85th percentile for very-high risk. CONCLUSIONS: Individually, WC or WHtR seem to be better markers for evaluating cardiometabolic risk than BMI during adolescence. However, CRF showed comparable importance to anthropometric markers in determining cardiometabolic risk. The simultaneous inclusion of anthropometric and CRF markers provides a better picture of the global cardiometabolic risk in adolescents.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".