Multiple breath washout: measuring early manifestations of lung pathology
Bibliographic record
Abstract
The multiple breath washout (MBW) test measures the efficiency of gas mixing in the lungs and has gained significant interest over the past 20 years. MBW outcomes detect early lung function impairment and peripheral airway pathology, through its main outcome measure lung clearance index (LCI). LCI measures the number of lung turnovers required to washout an inert tracer gas. MBW is performed during normal (tidal) breathing, making it particularly suitable for young children or those who have trouble performing forced manoeuvres. Additionally, research in chronic respiratory disease populations has shown that MBW can detect acute clinically relevant changes before conventional lung function tests, such as spirometry, thus enabling early intervention. The development of technical standards for MBW and commercial devices have allowed MBW to be implemented in clinical research and potentially routine clinical practice. Although studies have summarised clinimetric properties of MBW indices, additional research is required to establish the clinical utility of MBW and, if possible, shorten testing time. Sensitive, feasible measures of early lung function decline will play an important role in early intervention for people living with respiratory diseases. EDUCATIONAL AIM: To describe the multiple breath washout test, its applications to lung pathology and respiratory disease, as well as directions for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".