Age‐ and size‐related reference ranges: A case study of spirometry through childhood and adulthood
Bibliographic record
Abstract
Age-related reference ranges are useful for assessing growth in children. The LMS method is a popular technique for constructing growth charts that model the age-changing distribution of the measurement in terms of the median, coefficient of variation and skewness. Here the methodology is extended to references that depend on body size as well as age, by exploiting the flexibility of the generalised additive models for location, scale and shape (GAMLSS) technique. GAMLSS offers general linear predictors for each moment parameter and a choice of error distributions, which can handle kurtosis as well as skewness. A key question with such references is the nature of the age-size adjustment, additive or multiplicative, which is explored by comparing the identity link and log link for the median predictor.There are several measurements whose reference ranges depend on both body size and age. As an example, models are developed here for the first four moments of the lung function variables forced expiratory volume in 1 s (FEV(1)), forced vital capacity (FVC) and FEV(1)/FVC in terms of height and age, in a data set of 3598 children and adults aged 4 to 80 years. The results show a strong multiplicative association between spirometry, height and age, with a large and nonlinear age effect across the age range. Variability also depends nonlinearly on age and to a lesser extent on height. FEV(1) and FVC are close to normally distributed, while FEV(1)/FVC is appreciably skew to the left. GAMLSS is a powerful technique for the construction of such references, which should be useful in clinical medicine.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".