Alveolar lung clearance index: An alternative multiple breath washout outcome in children
Bibliographic record
Abstract
Background: Higher lung clearance index (LCI) values observed on the Ecomedics Multiple Breath Nitrogen Washout (N 2 MBW) relative to the respiratory mass spectrometry MBW (SF 6 MBW), may be partially explained by larger equipment dead space of the N 2 MBW (58.2 versus 15.4 ml). In this study we investigate whether calculation of alveolar LCI (aLCI), whereby airways dead space is accounted for, results in LCI values that more closely agree between the two systems. Methods: 50 healthy children and 42 children with CF (6 to 18 years) had both N 2 MBW and SF 6 MBW measured on the same test occasion. Standard LCI (sLCI) was calculated by correcting for total equipment dead space. In addition, we calculated alveolar LCI (aLCI), whereby calculations corrected for airways dead space (from the first breath of the washout using the Fowler method) and equipment dead space. The average sLCI and aLCI were calculated from all good quality trials, and compared. Results: On average aLCI was lower than sLCI on both the N 2 (aLCI=7.6(SD=3.1), sLCI =9.0(3.6)) and SF 6 (aLCI=7.2(2.7), sLCI=8.1(2.9)) systems. The difference in aLCI (Δ=-0.62(1.0)) between N 2 MBW and SF 6 MBW was in the opposite direction to that observed for sLCI (Δ= 0.77 (1.1)) which is likely due to the underestimation of FRC by SF 6 MBW (Jensen et al., 2013). The magnitude of the differences between systems was greater in CF than in health. Conclusions: These results suggest that higher LCI values measured by N 2 MBW may be partially explained by the larger equipment dead space relative to SF 6 MBW. This may be especially relevant in young children, where the ratio of equipment dead space to tidal volumes is high. Funded by CF Foundation, Irwin Family 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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".