Predictive Formulas To Improve The Interpretation Of Cardiorespiratory Fitness In Children
Bibliographic record
Abstract
Adequate reference values for cardiopulmonary exercise testing (CPET) is crucial for accurate interpretation and prognostic purposes for children with a chronic disease. Current reference values in healthy children have been developed using heterogeneous exercise protocols and often incomplete adjustment for body size. PURPOSE: To update current reference values from CPET and provide new reference values for several parameters previously unstudied in children using a prospectively recruited cohort of healthy children. METHODS: In this cross-sectional multicenter study, we prospectively recruited 269 healthy children (♂=107; ♀=162) between the ages of 12-17 years old (14.8 ± 1.5) in local schools. We measured height, weight, waist circumference, pubertal development and fat free mass (FFM) and performed a symptom-limited CPET (Vmax Encore Metabolic Cart, Sensormedic, San Diego, CA) on an electronically-braked ergocycle using a progressive ramp protocol. Reference values and Z score were computed by testing several regressions models for each CPET measurement. Variation around the predicted mean was modeled to account for heteroscedasticity and residual association with growth-related parameters was assessed. RESULTS: Using currently published reference values, up to 31.2% of children were classified as having abnormal CPET results despite being free of chronic disease. Our weighted non-linear parametric modeling allowed more precise and well-adjustted Z scores and percentiles limits. The table shows a selection of our predicting equations as well as the percentage of children below the 3rd percentile. Selection of prediction equations for malesTable: No title available.CONCLUSION: The use of weighted non-linear regression model resulted in a decreased false-positive rate. These updated and new reference values provide an accurate lower limit of normal thus improving their value for prognostic and risk-stratification 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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".