An update on multiple breath washout in children with cystic fibrosis
Bibliographic record
Abstract
INTRODUCTION: Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CF transmembrane regulator (CFTR) gene, leading to progressive lung disease and systemic complications. Lung disease remains the primary cause of morbidity and mortality, making early detection of lung function decline crucial. The Lung Clearance Index (LCI), derived from the multiple breath washout (MBW) test, has emerged as a sensitive measure for identifying early airway disease. AREAS COVERED: This review examines the technical aspects and clinical relevance of LCI, its advantages over traditional lung function tests, and its application in CF clinical trials. A focused literature review highlights LCI's utility in evaluating treatment efficacy and its potential integration into routine CF care. EXPERT OPINION: LCI is more sensitive than spirometry for detecting early lung function decline and is predominantly used in pediatric settings. Its use is expanding in adult CF populations as advances in treatment allow adults to maintain stable lung function. In clinical trials, LCI is widely recognized as an outcome measure. While implemented into clinical care in many centers in Europe, this is not yet the case in North America. Faster testing protocols and point-of-care interpretation tools will support LCI's integration into routine CF monitoring.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".