CFTR-function and ventilation inhomogeneity in individuals with cystic fibrosis
Bibliographic record
Abstract
Background Increased (abnormal) ventilation inhomogeneity in individuals with mild Cystic Fibrosis (CF) lung disease may become a treatable trait for small-molecule therapeutics improving Cystic Fibrosis Transmembrane Regulator (CFTR) function. The relationship between CFTR function and ventilation inhomogeneity is unknown. We aimed to identify and quantify increased ventilation inhomogeneity in relation to CFTR function. Methods This was an international, multi-center, cross-sectional study. We collated data from individuals aged 3–25 years with minimal (CFTR-MF) or residual (CFTR-RF) function of a variety of CFTR genotypes and FEV 1 ≥ 70% predicted. We measured lung function using nitrogen multiple-breath washout and spirometry. We compared lung clearance index (LCI) and FEV 1 between individuals with CFTR-MF vs CFTR-RF using a mixed effects multi-variable linear regression model to account for study differences and a logistic model based on propensity-score matching to adjust for possible confounding. Results We included 141 with CFTR-MF and 35 with CFTR-RF. LCI (> 1.96 z-score) was elevated in 71.6% individuals with CFTR-MF and in 40.0% with CFTR-RF. FEV 1 (< -1.96 z-score) was reduced in 11.3% individuals with CFTR-MF and in 5.7% with CFTR-RF. The mean difference (95% CI) of LCI and FEV 1 between CFTR-MF and CFTR-RF was 3.71 (1.63 to 5.79) and -0.40 (-0.83 to 0.02) z-score. The LCI differences were similar after adjustment for confounders and in individuals with normal FEV 1 . Conclusion Increased ventilation inhomogeneity is associated with less CFTR function. In individuals with mild CF lung disease, LCI can identify and quantify increased ventilation inhomogeneity, a candidate treatable trait.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".