A Systematic Approach to Multiple Breath Nitrogen Washout Test Quality
Bibliographic record
Abstract
BACKGROUND: Accurate estimates of multiple breath washout (MBW) outcomes require correct operation of the device, appropriate distraction of the subject to ensure they breathe in a manner representative of their relaxed tidal breathing pattern, and appropriate interpretation of the acquired data. Based on available recommendations for an acceptable MBW test, we aimed to develop a protocol to systematically evaluate MBW measurements based on these criteria. METHODS: 50 MBW test occasions were systematically reviewed for technical elements and whether the breathing pattern was representative of relaxed tidal breathing by an experienced MBW operator. The impact of qualitative and quantitative criteria on inter-observer agreement was assessed across eight MBW operators (n = 20 test occasions, compared using a Kappa statistic). RESULTS: Using qualitative criteria, 46/168 trials were rejected: 16.6% were technically unacceptable and 10.7% were excluded due to inappropriate breathing pattern. Reviewer agreement was good using qualitative criteria and further improved with quantitative criteria from (κ = 0.53-0.83%) to (κ 0.73-0.97%), but at the cost of exclusion of further test occasions in this retrospective data analysis. CONCLUSIONS: The application of the systematic review improved inter-observer agreement but did not affect reported 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 | 0.637 | 0.811 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.033 | 0.017 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".