Lung clearance index is a sensitive, repeatable and practical measure of airways disease in adults with cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: Lung clearance index (LCI) is a sensitive marker of early lung disease in children but has not been assessed in adults. Measurement is hindered by the complexity of the equipment required. The aims of this study were to assess performance of a novel gas analyser (Innocor) and to use it as a clinical tool for the measurement of LCI in cystic fibrosis (CF). METHODS: LCI was measured in 48 healthy adults, 12 healthy school-age children and 33 adults with CF by performing an inert gas washout from 0.2% sulfur hexafluoride (SF6). SF6 signal:noise ratio and 10-90% rise time of Innocor were compared with a mass spectrometer used in similar studies in children. RESULTS: Compared with the mass spectrometer, Innocor had a superior signal:noise ratio but a slower rise time (150 ms vs 60 ms) which may limit its use in very young children. Mean (SD) LCI in healthy adults was significantly different from that in patients with CF: 6.7 (0.4) vs 13.1 (3.8), p<0.001. Ten of the patients with CF had forced expiratory volume in 1 s > or = 80% predicted but only one had a normal LCI. LCI repeats were reproducible in all three groups of subjects (mean intra-visit coefficient of variation ranged from 3.6% to 5.4%). CONCLUSIONS: Innocor can be adapted to measure LCI and affords a simpler alternative to a mass spectrometer. LCI is raised in adults with CF with normal spirometry, and may prove to be a more sensitive marker of the effects of treatment in this group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".