Inter-test reproducibility of the lung clearance index measured by multiple breath washout
Bibliographic record
Abstract
The lung clearance index (LCI) has strong intra-test repeatability; however, the inter-test reproducibility of the LCI is poorly defined.The aim of the present study was to define a physiologically meaningful change in LCI in preschool children, which discriminates changes associated with disease progression from biological variability.Repeated LCI measurements from a longitudinal cohort study of children with cystic fibrosis and age-matched controls were collected to define the inter-visit reproducibility of the LCI. Absolute change, the coefficient of variation, Bland-Altman limits of agreement, the coefficient of repeatability, intra-class correlation coefficient, and percentage changes were calculated.LCI measurements (n=505) from 71 healthy and 77 cystic fibrosis participants (aged 2.6-6 years) were analysed. LCI variability was proportional to its magnitude, such that reproducibility defined by absolute changes is biased. A physiologically relevant change for quarterly LCI measurements in health was defined as exceeding ±15%. In clinically stable cystic fibrosis participants, the threshold was higher (±25%); however, for measurements made 24 h apart, the threshold was similar to that observed in health (±17%).A percentage change in LCI greater than ±15% in preschool children can be considered physiologically relevant and greater than the biological variability of the test.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".