Increased within-test variability may bias estimates of the LCI
Bibliographic record
Abstract
Background: The Multiple Breath Washout (MBW) test has been shown to be an important research tool and has the potential for wider use in clinical practice. Feasibility for pediatric clinical settings may be improved if the testing time can be shortened.We investigated whether it is possible to minimize the number of trials performed, without affecting the precision of the test. Method: 42 healthy and 37 children with CF performed nitrogen MBW (Exhalyzer D, Eco Medics AG, Switzerland). The mean LCI and coefficient of variation (CV) were calculated from all technically acceptable trials (i.e. no obvious leaks, end of test criteria met, etc., with some adaptations for children). In addition, the first acceptable trial, and the best trial (defined as the first acceptable trial with a breathing pattern most reflective of quiet tidal breathing) were selected. The mean LCI was compared to the LCI of the first, as well as the best trial. Results: As the CV increased, the magnitude of the difference between the mean LCI and first LCI also increased. Compared to the best LCI, the mean LCI was higher in 70% of subjects. The difference between the best LCI and mean LCI also increased as the CV increased. Using the best LCI as a benchmark, 70% of subjects had a reproducible LCI within 5% and 97% within 10%. Conclusions: These findings suggest that the average LCI from several MBW trials can over-estimate LCI, especially if the variability between trials is high. Reporting the LCI from the best trial, which reflects an ideal quiet tidal breathing pattern, may minimize testing time and improve the accuracy of the test without affecting precision. In addition the CV may be a useful quality control measure.
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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 | 0.081 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".