Evaluation of a multiple breath nitrogen washout system in children
Bibliographic record
Abstract
Abstract Introduction The multiple breath nitrogen washout (MBW) test offers a sensitive measure of airway function. In this study we aim to (a) assess the validity of the EasyOne Pro LAB (MBWndd) in an in vitro lung model, (b) assess the feasibility, repeatability, and reproducibility of MBWndd and (c) compare outcomes with the Exhalyzer D (MBWEM) and body plethysmography. Methods In vitro, functional residual capacity (FRC) measurements were assessed using a lung model under quasi‐physiological conditions and compared to measured FRC. In vivo plethysmography and MBW were performed in a prospective study of children at two visits (n = 45 healthy; n = 41 cystic fibrosis [CF]). Bland‐Altman plots were used to compare agreement between FRC and lung clearance index (LCI) measurements. Results In vitro FRCndd measurements were repeatable but lung volumes were underestimated (mean relative difference −5.4% (limits of agreement [LA] −9.6%; −1.1%), 95% confidence interval (CI) −6.27; −4.45). In vivo, compared to plethysmography, FRCndd was consistently lower (−19.3% [−40.5; 1.9], 95% CI [−23.9; −14.7]), and showed a volume dependency. LCIndd values were also higher in children with smaller lung volumes. The within‐test coefficient of variation of the FRCndd and LCIndd were 4.9% in health, and 5.6% and 6.9% in CF respectively. LCIndd was reproducible between‐visits (mean relative difference [LA] −3.7% [−14.8, −7.5; 95% CI −6.6; −0.73] in health [n = 17] and 0.34% [−13.2, 22.8; 95% CI −5.0; 5.69] in CF [n = 23]). When calculated using the same algorithm, LCIndd was similar to LCIEM in health. Conclusions MBWndd measurements are feasible, repeatable, and reproducible, however, MBW‐derived outcomes are not interchangeable with MBWEM.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 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".