Assessing the feasibility of hyperpolarized <sup>129</sup>Xe multiple‐breath washout MRI in pediatric cystic fibrosis
Bibliographic record
Abstract
Purpose To assess the feasibility of hyperpolarized 129Xe multiple‐breath washout MRI in pediatric cystic fibrosis (CF) participants with preserved lung function. Fractional ventilation (r), defined as the fractional gas replacement per breath, was mapped using 2 signal models: (1) constant T1 and (2) variable T1 as a function of the hyperpolarized gas washout. Methods A total of 17 pediatric participants were recruited (mean age 11.7 ± 2.8 years), including 7 children with clinically stable CF and 10 aged‐matched healthy controls. Pulmonary function tests were performed, including spirometry, to measure the forced expiratory volume in 1 second and nitrogen multiple‐breath washout to measure the lung clearance index. Hyperpolarized 129Xe MRI was performed during consecutive breaths of air following a single 129Xe inhalation, and fractional ventilation maps were calculated. Results The forced expiratory volume in 1 second was similar in both groups (P = .32), but there was a statistically significant difference in lung clearance index between healthy and CF participants (P = .001). With variable T1 modeling, CF participants had a mean r of 0.44 ± 0.08 and healthy participants had a mean r of 0.37 ± 0.12 (P = .20). With constant T1 modeling, CF participants had a mean r′ of 0.48 ± 0.08, and healthy participants had a mean r′ of 0.43 ± 0.12 (P = .32). Therefore, assuming a constant T1 leads to a relative bias in r of 15.1% ± 6.4% and 20.8% ± 7.4% for CF and healthy participants, respectively (P = .12). Conclusion This study demonstrates that hyperpolarized 129Xe multiple‐breath washout imaging is feasible in pediatric participants with CF, and inclusion of variable T1 modeling reduces bias in the fractional ventilation measurements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".