Lung compartment analysis assessed from N<sub>2</sub> multiple‐breath washout in children with cystic fibrosis
Bibliographic record
Abstract
Abstract Background Compartment analysis (CA) based on nitrogen multiple‐breath washout (N2MBW) has been shown to allow the assessment of specific volume and ventilation of faster‐ and slower‐ventilating lung compartments of the lung in adults with cystic fibrosis (CF). The aim of this study was to extend previous findings into the pediatric age range. Methods Cross‐sectional multicenter observational study in children with CF and healthy controls (HC) was done with the assessment of N2MBW and spirometry. A two‐lung compartment model‐based analysis (CA) was used to estimate size and function of faster‐ and slower‐ventilating lung compartments from N2MBW. Results A total of 125 children with CF and 177 HC, median age 10.8 (range, 2.8‐18.9) years, were included in the analysis. CA could be calculated in 66 (53%) children with CF compared with 48 (27%) HC (P < .0001). The proportion of the slower‐ventilating lung compartment was significantly smaller in children with CF (53.5%; 95% confidence interval [CI]: 51.9%‐55.7%) compared with HC (62.2%; 95% CI: 59.0%‐65.0%) The regional specific ventilation of the slower compartment (rVT,slow/rFRC,slow, %) was significantly lower in children with CF (4.9%; 95% CI: 4.5‐5.9) compared with HC (9.7%, 95% CI: 9.2‐10.9), and showed inverse correlation to lung clearance index (r2 = −.65; P < .0001), Sacin × VT (r2 = −.36; P = .003) and Scond × VT (r2 = −.51; P < .0001). There was no significant difference in pulmonary parameters between children with CF with and without feasible CA. Conclusion CA is less feasible in children than in adults and correlated to other MBW parameters. The clinical value of CA is still unclear and is yet to be established.
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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) | 0.001 | 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 | 0.000 | 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".