Effect of changes in tidal volume on multiple breath washout outcomes
Bibliographic record
Abstract
The lung clearance index (LCI), measured by multiple breath washout (MBW), reflects global ventilation inhomogeneity and is a sensitive marker of early obstructive airway disease. For the MBW test to accurately reflect a subject's gas mixing within the lungs, the breathing pattern should represent physiologically appropriate tidal volumes (VT) and respiratory rate (RR). We aimed to assess whether changes in VT impact MBW outcome measures with a series of prospective and retrospective studies. MBW testing was performed using the Exhalyzer ® D (EcoMedics AG, Switzerland). Healthy adult subjects performed MBW with uninstructed tidal breathing and a series of instructed tidal breathing tests, designed to isolate specific features of the breathing pattern. In addition, we retrospectively analyzed MBW data from two pediatric multi-centre interventional studies of cystic fibrosis (CF) subjects to determine the range of VT observed during uninstructed breathing, and whether breathing outside this range impacted results. The LCI was lower, but not significantly different between deep breathing at 20 ml/kg body weight and uninstructed tidal breathing; whereas LCI was significantly higher during shallow breathing compared with normal tidal breathing. For the majority of subjects with CF (80%), VT ranged from 9-15mL/kg. Within the observed VT range, LCI was similar in trials with mean VT /kg below this range compared to trials with VT /kg within the range. If subjects breathe naturally and are not instructed to use specific targets, the range of VT is within physiologically appropriate limits and normal variations observed do not impact MBW outcomes.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".