Do deviations in tidal breathing impact multiple breath washout measurements?
Bibliographic record
Abstract
Background: During Multiple Breath Washout (MBW) subjects are to maintain quiet, tidal breathing and avoid sighs. Most children naturally breathe at 8-13mL/kg of ideal body weight (IBW; Horton et al. 2017 Abstract) and breathing far outside this range may impact MBW results (Yammine et al. J Cyst Fibros 2014). Objective: To identify the distribution of tidal volumes during MBW testing in children and to assess whether natural deviations from the ideal range affect MBW results. Methods: 7100 MBW trials from two multicentre clinical studies without leaks and meeting end of test criteria were analyzed (43% male, median 8.1 years, range 2.2-15.3). Comparisons were made within-test, between trials in and outside the ideal range. IBW was calculated from sex and height using equations derived from CDC growth charts. Results: Median tidal volume was 11.97mL/kg IBW (IQR 10.65-13.45). 2248 trials (32%) fell above 13mL/kg IBW by a median of 1.2mL/kg IBW (IQR 0.56-2.4), and 131 (2%) fell below 8mL/kg IBW by a median of 0.40 mL/kg IBW (IQR 0.17-0.69). Breathing above 13mL/kg IBW had no impact on LCI (0.26%, 95% CI -0.45-0.98, p=0.47), but increased FRC by 2.97% (95% CI 2.28-3.68, n=910, P<0.01). Breathing below 8mL/kg IBW did not impact either LCI (p=0.24) or FRC (p=0.54). Trials with one or more sighs (defined as 150% of the mean tidal volume) were associated with an increase in LCI of 1.03% (95% CI 0.5-1.5, n=1862, p<0.01) and FRC of 2.31% (95% CI 1.87-2.75, n=1862, p<0.01). Conclusion: Within the range of tidal volumes observed, minor deviations from ideal tidal volume range, and sighs that do not impact end-tidal nitrogen concentration, had small effects on FRC and LCI that are unlikely to be clinically relevant.
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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 | 0.080 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".