Tracking Respiratory Mechanics With Oscillometry: Introduction of Time-Varying Error
Bibliographic record
Abstract
Oscillometry is a useful measure of lung function and recently has been used to estimate temporal variation in physiological mechanical properties of the lung, but to date, analysis methods assume stationarity or short-time stationarity despite substantial temporal variation, particularly in disease. The effect of time-varying parameters on the accuracy of estimates of impedance has not been previously analyzed. In this paper we analytically, computationally, and with added experimental data from seven children with asthma, assess the time-frequency transfer function of the time-varying respiratory system. We then evaluate the accuracy in determining the time-varying parameters of respiratory impedance. We introduce, for the first time, the error arising from respiratory time variation, termed the time-varying error (TVE) and demonstrate how TVE unexpectedly increases with increasing breathing rate independent of breathing noise amplitude. For breathing rates less than 0.4 Hz, we found that common analysis methods could be moderately accurate with less than 5% error. Since tracking time-varying impedance shows strong potential for assessing the severity of respiratory disease, it is import to recognize that errors should be avoided, or compensation circuits and systems developed based on the TVE, particularly important in patients with high variation or breathing rate such as children and infants.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".