Lung clearance index to monitor treatment response in pulmonary exacerbations in preschool children with cystic fibrosis
Bibliographic record
Abstract
Background Antibiotic treatment for pulmonary symptoms in preschool children with cystic fibrosis (CF) varies among clinicians. The lung clearance index (LCI) is sensitive to early CF lung disease, but its utility to monitor pulmonary exacerbations in young children has not been assessed. Objective We aim to (1) understand how LCI changes during lower respiratory tract symptoms relative to a recent clinically stable measurement, (2) determine whether LCI can identify antibiotic treatment response and (3) compare LCI changes to changes in spirometric indices. Methods LCI and spirometry were measured at quarterly clinic visits over a 12-month period in preschool children with CF. Symptomatic visits were identified and classified as treated or untreated. Treatment response was estimated using propensity score matching methods. Results 104 symptomatic visits were identified in 78 participants. LCI increased from baseline in both treated (mean relative change +23.8% (95% CI 16.2 to 31.4)) and untreated symptomatic visits (mean relative change +11.2% (95% CI 2.4 to 19.9)). A significant antibiotic treatment effect was observed when LCI was used as the outcome measure (average treatment effect −15.5% (95% CI −25.4 to −5.6)) but not for z-score FEV1. Conclusion LCI significantly deteriorated with pulmonary symptoms relative to baseline and improved with antibiotic treatment. These data suggest that LCI may have a role in the routine clinical care of preschool children with 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".