Does the lung clearance index track with disease progression in early childhood?
Bibliographic record
Abstract
Background: The lung clearance index (LCI) is sensitive in detecting early cystic fibrosis (CF) lung disease; its ability to track disease progression is not well defined. The Infant Study of Inhaled Saline (ISIS) demonstrated a positive treatment effect of hypertonic (HS) versus isotonic saline on LCI (Subbarao P et al, AJRCCM 2013), but the long-term effect of this intervention is presently unknown. Objective: To investigate whether changes in LCI in early childhood are associated with clinical markers of CF lung disease and treatment with HS. Methods: All 26 patients that participated in the ISIS multiple breath washout (MBW) sub-study were invited for a follow-up measurement. MBW was performed using sulfurhexafluoride as a tracer gas and a mass spectrometrer (AMIS, Odense, Denmark) for all visits. LCI was corrected for dead space using a polynomial equation (Benseler A et al, Respirology 2015). Associations between change in LCI and age, gender, medication use (dornase alfa, HS), pulmonary infection (P. aeruginosa), exacerbations assessed by number of antibiotic courses, and nutritional status was investigated using generalized estimation equations. Results: 22 patients performed a follow-up measurement 2.2-5.2 years after the first ISIS measurement (median age at follow-up: 6.2 years (95% CI: 4.0 – 9.6)). 10 or more oral antibiotic courses and use of dornase alfa were associated with higher LCI values, whereas better nutritional status was associated with lower LCI values. HS use was not associated with longitudinal changes in LCI. Conclusion: We observed associations with known risk factors for poorer lung function supporting the utility of LCI as a measure to track lung function in CF.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".