Effect of reporting two versus three trials on lung clearance index values
Bibliographic record
Abstract
Introduction: Currently, it is recommended that the lung clearance index (LCI) be reported as the average from three technically acceptable multiple breath washout (MBW) trials (Robinson et al., ERJ 2013). The objective of this study was to determine whether reporting two trials produces similar LCI results to three trials. Methods: MBW data collected using the Exhalyzer D® (EcoMedics AG, Switzerland) from one longitudinal study and one multi-centered interventional study were used for this analysis. Both studies requested MBW operators to collect at least three trials at each visit. All MBW data were over-read for technical quality by experienced reviewers. Success rates, average LCI, and the % coefficient of variation (CV) of LCI were compared using two or three trials as acceptability criteria. Results: Data included 1385 visits from 294 children aged 2.5-11 years in the two studies. Overall success was higher with two trials (84.6%) than three trials (60.4%) (Δ 24.2%; 95% CI 21.0, 27.4%; p<0.001). For test occasions with three acceptable trials (n=835), mean (SD) LCI was similar if two or three trials were included (8.4 vs 8.4; Δ 0.0; 95% CI -0.1, 0.2; p=0.79). The % CV was lower using two trials (4.5 vs 5.1, Δ -0.6; 95% CI -1.1, -0.1; p=0.01). Conclusion: Reporting LCI results from at least two technically acceptable trials allows for over 20% more visits to be included in analysis without affecting the value or the precision of the LCI.
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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 | 0.601 | 0.824 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".