Late Breaking Abstract - ERS technical standard: Global Lung Function Initiative reference values for lung clearance index and functional residual capacity
Bibliographic record
Abstract
Background: Multiple breath washout is a lung function test that assesses lung volume and ventilation distribution. The aim of this analysis was to use the Global Lung Function Initiative methodology to develop all-age reference equations for the multiple breath washout indices lung clearance index (LCI) and functional residual capacity (FRC). Methods: Multiple breath washout data from healthy individuals were collated. Data were re-analysed using the latest software versions. Reference equations were derived using the lambda-mu-sigma (LMS) method. The impact of equipment type, inert tracer gas, and equipment dead space volume on the derived reference ranges were investigated. Results: Data from 23 sites (n=3647 test occasions) were submitted. Reference equations were derived from 1581 unique observations from participants between the ages of 2 and 81 years. Equipment type, inert tracer gas, and equipment dead space volume did not significantly affect the prediction equations for either LCI or FRC. Reference equations for LCI include age as the only predictor, whereas sex-specific reference equations for FRC included height and age (Figure 1). Conclusions: Global Lung Function Initiative all-ages reference equations for LCI and FRC improve our understanding of how these outcomes evolve with age in healthy subjects and provide a new standard for reporting and interpretation. Funding: ERS TF-2020-01
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.052 |
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".