Multiple-breath inert gas washout and spirometry versus structural lung disease in cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: A sensitive and valid non-invasive marker of early cystic fibrosis (CF) lung disease is sought. The lung clearance index (LCI) from multiple-breath washout (MBW) is known to detect abnormal lung function more readily than spirometry in children and teenagers with CF, but its relationship to structural lung abnormalities is unknown. A study was undertaken to determine the agreements between LCI and spirometry, respectively, with structural lung disease as measured by high-resolution computed tomography (HRCT) in children and teenagers with CF. METHODS: A retrospective study was performed in 44 consecutive patients with CF aged 5-19 years (mean 12 years). At an annual check-up inspiratory and expiratory HRCT scans, LCI and spirometric parameters (forced expiratory volume in 1 s (FEV1) and maximal expiratory flow when 75% of forced vital capacity was expired (FEF75)) were recorded. Abnormal structure was defined as a composite HRCT score of >5%, the presence of bronchiectasis or air trapping >30%. Abnormal lung function was defined as LCI above the predicted mean +1.96 residual standard deviations (RSD), or FEV1 or FEF75 below the predicted mean -1.96 RSD. Sensitivity/specificity assessments and correlation analyses were done. RESULTS: The sensitivity to detect abnormal lung structure was 85-94% for LCI, 19-26% for FEV1 and 62-75% for FEF75. Specificity was 43-65% for LCI, 89-100% for FEV1 and 75-88% for FEF75. LCI correlated better with HRCT scores (Rs +0.85) than FEV1 (-0.62) or FEF75 (-0.66). CONCLUSIONS: LCI is a more sensitive indicator than FEV1 or FEF75 for detecting structural lung disease in CF, and a normal LCI almost excludes HRCT abnormalities. The finding of an abnormal LCI in some patients with normal HRCT scans suggests that LCI may be even more sensitive than HRCT scanning for detecting lung involvement in CF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".