Comparison of oscillometry devices using active mechanical test loads
Bibliographic record
Abstract
Noninvasiveness, low cooperation demand and the potential for detailed physiological characterisation have promoted the use of oscillometry in the assessment of lung function. However, concerns have been raised about the comparability of measurement outcomes delivered by the different oscillometry devices. The present study compares the performances of oscillometers in the measurement of mechanical test loads with and without simulated breathing. Six devices (five were commercially available and one was custom made) were tested with mechanical test loads combining resistors (R), gas compliances (C) and a tube inertance (L), to mimic respiratory resistance (Rrs) and reactance (Xrs) spectra encountered in clinical practice. A ventilator was used to simulate breathing at tidal volumes of 300 and 700 mL at frequencies of 30 and 15 min−1, respectively. Measurements were evaluated in terms ofR,C,L, resonance frequency (fres), reactance area (AX) and resistance change between 5 and 20 or 19 Hz (R5–20(19)). Increasing test loads caused progressive deviations inRrsandXrsfrom calculated values at various degrees in the different oscillometers. While mean values ofRrswere recovered acceptably, some devices exhibited serious distortions in the frequency dependences ofRrsandXrs, leading to large errors inC,L,fres,AXandR5–20(19). The results were largely independent of the simulated breathing. Simplistic calibration procedures and mouthpiece corrections, in addition to unknown instrumental and signal processing factors, may be responsible for the large differences in oscillometry measures. Rigorous testing and ongoing harmonisation efforts are necessary to better exploit the diagnostic and scientific potential of oscillometry.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| 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".