The total-breath method yields higher values of DLCO and TLC than the conventional method
Bibliographic record
Abstract
Abstract Background The 2017 ATS/ERS technical standard for measuring the single-breath diffusing capacity (DLCO) proposed the “rapid-gas-analyzer” (RGA) or, equivalently, “total-breath” (TB) method for the determination of total lung capacity (TLC). In this study, we compared DLCO and TLC values estimated using the TB and conventional method, and how estimated TLC using these two methods compared to that determined by body plethysmography. Method A total of 95 people with COPD (GOLD grades 1–4) and 23 healthy subjects were studied using the EasyOne Pro (ndd Medical Technologies, Switzerland) and Master Screen Body (Vyaire Medical, Höchberg, Germany). Results On average the TB method resulted in higher values of DLCO (mean ± SD Δ = 0.469 ± 0.267; 95%CI: 0.420; 0.517 mmol*min-1*kPa-1) and TLC (Δ = 0.495 ± 0.371; 95%CI: 0.427; 0.562 L) compared with the conventional method. In healthy subjects the ratio between TB and conventional DLCO was close to one. TLC estimated using both methods was lower than that determined by plethysmography. The difference was smaller for the TB method (Δ = 1.064 ± 0.740; 95%CI: 0.929; 1.199 L) compared with the conventional method (Δ = 1.558 ± 0.940; 95%CI: 1.387; 1.739 L). TLC from body plethysmography could be estimated as a function of TB TLC and FEV1 Z-Score with an accuracy (normalized root mean square difference) of 9.1%. Conclusion The total-breath method yielded higher values of DLCO and TLC than the conventional analysis, especially in subjects with COPD. TLC from the total-breath method can also be used to estimate plethysmographic TLC with better accuracy than the conventional method. The study is registered under clinicaltrial.gov NCT04531293.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".