New Reference Values for Cardiopulmonary Exercise Testing in Children
Bibliographic record
Abstract
INTRODUCTION: Cardiopulmonary exercise testing is an essential tool to assess cardiorespiratory fitness (CRF) in children. There is a paucity of adequate pediatric reference values that are independent of body size and pubertal stage. The purpose of this study is to provide Z score equations for several maximal and submaximal CRF parameters derived from a prospectively recruited sample of healthy children. METHODS: In this cross-sectional multicenter study, we prospectively recruited 228 healthy children 12 to 17 yr old in local schools. We performed a symptom-limited cardiopulmonary exercise testing progressive ramp protocol on an electronically braked cycle ergometer. Eighteen CRF parameters were analyzed. We tested several regression models to obtain prediction curves that minimized residual association with age, body size, and pubertal stage. Both the predicted mean and the predicted SD were modeled to account for heteroscedasticity. RESULTS: We identified nonlinear association of CRF parameters with body size and significant heteroscedasticity. To normalize CRF parameters, the use of a single body size variable was not sufficient. We therefore used multivariable models with various combination of height, corrected body mass, and age. Final prediction models yielded adjusted CRF parameters that were independent of age, sex, body mass, height, body mass index, and Tanner stages. CONCLUSIONS: We present Z score equations for several CRF parameters derived from a healthy pediatric population. These reference values provide updated predicted means and range of normality that are independent of sex and body size. Further testing is needed to assess if these reference values increase sensitivity and specificity to identify abnormal cardiorespiratory response in children with chronic diseases.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".