Correcting for tissue nitrogen excretion in multiple breath washout measurements
Bibliographic record
Abstract
Nitrogen excreted from body tissues impacts the calculation of multiple breath nitrogen washout (MBWN2) outcomes. The aim of this study was to determine the effect of tissue N2 on MBWN2 outcomes in both healthy subjects and patients with CF and to assess whether it is possible to correct for tissue N2. The contribution of tissue N2 to MBWN2 outcomes was estimated by comparing MBWN2-derived functional residual capacity (FRCN2) to FRC measured by body plethysmography (FRCpleth) and by comparing MBW outcome measures derived from MBWN2 and sulfur hexafluoride MBW (MBWSF6). Compared to plethysmography and MBWSF6, MBWN2 overestimated FRC and lung clearance index (LCI). Application of mathematical tissue N2 corrections reduced FRCN2 values closer to FRCpleth in health and reduced LCIN2 in both health and CF, but did not explain all of the differences observed between N2-dependent and -independent techniques. Use of earlier washout cut-offs could reduce the influence of tissue N2. Applying tissue N2 corrections to LCIN2 measurements did not significantly affect the interpretation of treatment effects reported in a previously published interventional trial. While tissue N2 excretion likely has an impact on MBWN2 outcomes, better understanding of the nature of this phenomenon is required before routine correction can be implemented into current MBWN2 protocols.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".