Ventilation distribution efficiency or lung clearance index: Does it matter?
Bibliographic record
Abstract
Background The ratio of 1/lung clearance index (LCI) has been proposed as an improved index of ventilation inhomogeneity as it may be less influenced by dead space ventilation than LCI (Sandvik R.M. et al. J Appl Physiol 2023). We aimed to investigate 1/LCI for longitudinal tracking to assess the influence of physiological dead space changes, and to capture differences between interfaces. Methods This is a secondary analysis of an observational study (Stanojevic, S. et al. Eur Respir J 2021) of 64 cystic fibrosis (CF) children and 48 healthy controls followed for 12 and 24 months during preschool and school-age, respectively. Nitrogen multiple breath washout (MBW) was performed quarterly with the Exhalyzer D device and reported from Spiroware 3.3.2. To assess the impact of equipment dead space, 30 children performed MBW tests using mask and mouthpiece on the same day, in random order. Results During preschool, both 1/LCI and LCI were stable in healthy children, while a small, yearly decrease in 1/LCI(%) (Δ-0.13, 95% CI -0.23, -0.02; p=0.02) and a corresponding increase in LCI (Δ0.04, 95% CI 0.00, 0.08; p=0.03) were found during school-age. Individual slopes were highly correlated in both healthy children and CF (r=0.96; p<0.001). Both 1/LCI (r= -0.41; p=0.01) and LCI (r= 0.41; p=0.01) correlated with dead space ventilation (Vd/Vt) in healthy preschool and school age children. Comparing mask vs. mouthpiece, we found decreased 1/LCI(%) (Δ-1.44 (95% CI -1.68, -1.20); p<0.001) and increased LCI (Δ0.62 (95% CI 0.51, 0.73; p<0.001) in children using masks. Conclusion In this longitudinal study, 1/LCI mimicked the changes in LCI and did not prove to be a more useful outcome measure for longitudinal tracking.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".