The effect of spirometry on multiple breath washout outcomes in children with cystic fibrosis
Bibliographic record
Abstract
Multiple breath nitrogen washout (MBW) measurements in children with cystic fibrosis (CF) are often performed prior to spirometry, since forced-expiratory maneuvers could affect MBW outcomes as previously demonstrated in infants (Subbarao et al. Pediatr Pulmonol 2016). The objective of this study was to determine effects of spirometry on MBW outcomes in school-age children with CF. Children with CF between 6 and 18 years hospitalized for treatment of pulmonary exacerbations were included. MBW was performed during the second week of hospital admission using the Exhalyzer D (Eco Medics AG, Duernten, Switzerland) prior to spirometry (Easy on-PC, Ndd, Zurich, Switzerland) and repeated immediately after. MBW outcomes included lung clearance index (LCI), forced residual capacity (FRC) and cumulative expired volume (CEV). Wilcoxon signed-rank test was used to compare MBW outcomes pre and post spirometry. Fifteen children with CF (median age of 15 years, 73% female) completed the protocol. The median LCI prior to spirometry was 13.1 (IQR, 11.4-16.8); LCI post-spirometry was 12.9 (IQR, 11.5-15.7). There was no significant difference in LCI after spirometry (mean difference 0.04, 95% CI -0.54-0.63; relative difference -2.0%; p=0.99). There was no difference in FRC (relative difference 1.9%; p=0.95) or CEV (relative difference 0.6%; p=0.65). Upon Bland Altman analysis, the observed difference in LCI was not related to its magnitude and was within the normal biological variability of the test (Oude Engberink et al. Eur Respir J 2017). Performing spirometry before MBW had no effect on outcomes and may allow for greater flexibility for its use in clinical practice. Supported by The Irwin Foundation
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".