A two-center analysis of hyperpolarized 129Xe lung MRI in stable pediatric cystic fibrosis: Potential as a biomarker for multi-site trials
Bibliographic record
Abstract
Background The ventilation defect percent (VDP), measured from hyperpolarized (HP) 129 Xe magnetic resonance imaging (MRI), is sensitive to functional changes in cystic fibrosis (CF) lung disease. The purpose of this study was to measure and compare VDP from HP 129 Xe MRI acquired at two institutions in stable pediatric CF subjects with preserved lung function. Methods This retrospective analysis included 26 participants from two institutions (18 CF, 8 healthy, age range 10–17). Pulmonary function tests, N 2 multiple breath washout (to measure lung clearance index, LCI), and HP 129 Xe MRI were performed. VDP measurements were compared between two trained analysts using mean-anchored linear binning. Correlations were investigated for VDP compared to the forced expiratory volume in one second (FEV 1 ) and LCI. Results VDP measurements agreed for the two analysts with an intraclass correlation coefficient of 0.99. In the combined dataset, VDP measured by Analyst 1 was 5.96 ± 1.82% and 15.96 ± 6.76% for the healthy and CF groups, respectively ( p = .0004). Analyst 2 showed similar differences between healthy and CF ( p = .0003). VDP measured by either analyst was shown to correlate with FEV 1 (R 2 = 0.33, p = .003; and R 2 = 0.26, p = .009 for Analysts 1 and 2, respectively) and LCI (R 2 = 0.76, p < .0001; and R 2 = 0.77, p < .0001 for Analysts 1 and 2, respectively). Conclusion HP 129 Xe MRI provides a robust measurement of ventilation heterogeneity in stable pediatric CF subjects at two sites. Since measurements performed at two sites yielded similar VDP values with near-identical values between different analysts, implementation of the technique in multi-center trials in CF appears feasible.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".