Determinants of lung disease progression measured by lung clearance index in children with cystic fibrosis
Bibliographic record
Abstract
The lung clearance index (LCI) measured by the multiple breath washout (MBW) test is sensitive to early lung disease in children with cystic fibrosis. While LCI worsens during the preschool years in cystic fibrosis, there is limited evidence to clarify whether this continues during the early school age years, and whether the trajectory of disease progression as measured by LCI is modifiable. A cohort of children (healthy and cystic fibrosis) previously studied for 12 months as preschoolers were followed during school age (5–10 years). LCI was measured every 3 months for a period of 24 months using the Exhalyzer D MBW nitrogen washout device. Linear mixed effects regression was used to model changes in LCI over time. A total of 582 MBW measurements in 48 healthy subjects and 845 measurements in 64 cystic fibrosis subjects were available. The majority of children with cystic fibrosis had elevated LCI at the first preschool and first school age visits (57.8% (37 out of 64)), whereas all but six had normal forced expiratory volume in 1 s (FEV1) values at the first school age visit. During school age years, the course of disease was stable (−0.02 units·year−1(95% CI −0.14–0.10). LCI measured during preschool years, as well as the rate of LCI change during this time period, were important determinants of LCI and FEV1,at school age. Preschool LCI was a major determinant of school age LCI; these findings further support that the preschool years are critical for early intervention strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".