Does the lung clearance index track from preschool to school age in children with cystic fibrosis?
Bibliographic record
Abstract
Background: As multiple breath washout (MBW) becomes more commonly used in research and clinical practice, there is a need to understand how the lung clearance index (LCI) changes in early childhood and tracks disease progression. Objective: To assess how LCI tracks from preschool to school age in healthy children and children with cystic fibrosis (CF). Methods: Children with CF and age-matched healthy controls previously enrolled in a longitudinal preschool study (Stanojevic et al. Am J Respir Crit Care Med 2017; 195: 1216-1225) were followed up during school age. LCI was compared between subjects’ last stable preschool visit and their first stable school age visit. MBW tests were performed with the Exhalyzer D (EcoMedics AD, Duernten, Switzerland) using age and size appropriate set ups. Results: At the time of this preliminary analysis, 29 children with CF and 35 healthy controls had successful MBW measurements at both a stable preschool and school age study visit. The mean (SD) age at their school age visit was 7.5 (1.2) and the median time between measurements was 2.5 years (range 1.3 – 3.6). Preschool and school age LCI were significantly correlated (r=0.67, p<0.001). Mean LCI decreased in health (Δ -4.7%; 95% CI -8.2, -1.3; p=0.008); changes in CF were more variable and not statistically significant (Δ -2.8; 95% CI –9.0, 3.5; p=0.37). The proportion of children with CF with an LCI < 8 remained consistent from preschool (20/29, 69%) to school age (19/29, 66%). Conclusions: LCI tracks between preschool and school age; however, interpretation of longitudinal LCI measurements should be made in context of changes seen even in healthy children. Supported by NHLBI and CFF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".