The clinical utility of lung clearance index in early cystic fibrosis lung disease is not impacted by the number of multiple-breath washout trials
Bibliographic record
Abstract
The lung clearance index (LCI) from the multiple-breath washout (MBW) test is a promising surveillance tool for pre-school children with cystic fibrosis (CF). Current guidelines for MBW testing recommend that three acceptable trials are required. However, success rates to achieve these criteria are low in children aged <7 years and feasibility may improve with modified pre-school criteria that accepts tests with two acceptable trials. This study aimed to determine if relationships between LCI and clinical outcomes of CF lung disease differ when only two acceptable MBW trials are assessed. Healthy children and children with CF aged 3-6 years were recruited for MBW testing. Children with CF also underwent bronchoalveolar lavage fluid collection and a chest computed tomography scan. MBW feasibility increased from 46% to 75% when tests with two trials were deemed acceptable compared with tests where three acceptable trials were required. Relationships between MBW outcomes and markers of pulmonary inflammation, infection and structural lung disease were not different between tests with three acceptable trials compared with tests with two acceptable trials. This study indicates that pre-school MBW data from two acceptable trials may provide sufficient information on ventilation distribution if three acceptable trials are not possible.
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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 | 0.096 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".