Lung Clearance Index as an Outcome Measure for Clinical Trials in Young Children with Cystic Fibrosis. A Pilot Study Using Inhaled Hypertonic Saline
Bibliographic record
Abstract
RATIONALE: Lung clearance index (LCI), measured by multiple breath washout (MBW), is a noninvasive measure of ventilation inhomogeneity that holds promise as an objective physiologic endpoint for clinical trials in infants and preschool children with cystic fibrosis (CF). OBJECTIVES: To study the feasibility of using LCI to assess treatment effect outcomes in CF trials of infants and preschoolers. METHODS: The Infant Study of Inhaled Saline trial was a multicenter, randomized, controlled trial of hypertonic (7%) versus isotonic (0.9%) saline inhaled twice daily for 48 weeks in children with CF under 6 years of age. LCI measurements were performed in a single-center pilot substudy at baseline and 48 weeks using a respiratory mass spectrometer and sulfur hexafluoride as the tracer gas. LCI measurements were standardized using published normative data (zLCI) to account for height-related changes in LCI during early childhood. A generalized estimating equation model with an interaction between treatment group and test occasion was used to estimate a treatment effect. MEASUREMENTS AND MAIN RESULTS: A total of 27 participants were randomized; 25 participants, aged (median [range]) 2.6 (0.34-4.95) years, had acceptable baseline and follow-up LCI measures. On average, LCI decreased in the hypertonic saline group (n = 12) by 1.19 z-scores units (95% confidence interval [CI] = -2.46 to 0.06), and remained stable in the isotonic saline group (n = 13) at 0.81 (95% CI = -0.40 to 2.02). A significant treatment effect was observed for zLCI (2.01; 95% CI = 0.26 to 3.76; P = 0.025). CONCLUSIONS: MBW testing is feasible in an interventional study in infants and preschool children with CF. These pilot findings support the development of MBW and LCI as an objective outcome measure in interventional trials in young children with CF, and provide estimates for sample size calculations for future studies.
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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 | 0.045 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".