Spirometry Centile Charts for Young Caucasian Children: The Asthma UK Collaborative Initiative
Bibliographic record
Abstract
RATIONALE: Advances in spirometry measurement techniques have made it possible to obtain measurements in children as young as 3 years of age; however, in practice, application remains limited by the lack of appropriate reference data for young children, which are often based on limited population-specific samples. OBJECTIVES: We aimed to build on previous models by collating existing reference data in young children (aged 3-7 yr), to produce updated prediction equations that span the preschool years and that are also linked to established reference equations for older children and adults. METHODS: The Asthma UK Collaborative Initiative was established to collate lung function data from healthy young children aged 3 to 7 years. Collaborators included researchers with access to pulmonary function test data in healthy preschool children. Spirometry centiles were created using the LMS (lambda, micro, sigma) method and extend previously published equations down to 3 years of age. MEASUREMENTS AND MAIN RESULTS: The Asthma UK centile charts for spirometry are based on the largest sample of healthy young Caucasian children aged 3-7 years (n = 3,777) from 15 centers across 11 countries and provide a continuous reference with a smooth transition into adolescence and adulthood. These equations improve existing pediatric equations by considering the between-subject variability to define a more appropriate age-dependent lower limit of normal. The collated data set reflects a variety of equipment, measurement protocols, and population characteristics and may be generalizable across different populations. CONCLUSIONS: We present prediction equations for spirometry for preschool children and provide a foundation that will facilitate continued updating.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".