Tracking of respiratory mechanics at multiple oscillation frequencies
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Intra-breath oscillometry (IBOsc) is an emerging approach to characterize dynamic changes in respiratory mechanical impedance (Zrs). IBOsc utilizes a small-amplitude sinusoidal signal superimposed on quiet breathing to track Zrs with sufficient temporal resolution to find specific time points, such as end-expiration (eE) and end-inspiration (eI). IBOsc has demonstrated superiority to conventional multifrequency oscillometry in detecting abnormal respiratory function and predicting future impairment in several clinical settings. The aim of the present study was to construct intra-breath Zrs spectra from multifrequency recordings to demonstrate how the Zrs spectrum and its measures change during breathing. METHODS: Conventional oscillometric recordings from groups of healthy subjects and patients with interstitial lung disease, asthma and chronic obstructive pulmonary disease (N=40 each group) were analyzed. Zrs was computed at each component of the multifrequency (5-37-Hz) signal to establish the Zrs spectra at eE and eI. This multi-frequency tracking method was validated on simulated Zrs data generated by a non-linear model of respiratory mechanics. The 2-way median test and Wilcoxon signed rank test were used to compare Zrs values and derived measures between groups and respiratory phases, respectively. RESULTS: Large intra-breath changes in Zrs were found in all subject groups. Most pairwise comparisons of Zrs measures (such as resistance, resonance frequency, reactance area and effective compliance) revealed significant (P<0.05) or highly significant (P<0.001) differences between groups at eE, which became more uniform at eI. Similarly, the changes between eE and eI were significant in most Zrs measures and subject groups, indicating the tidal improvement of lung mechanics in the obstructive patients. CONCLUSIONS: Our results demonstrate that re-processing of archived datasets is feasible and can provide useful additional data to further characterize respiratory mechanical phenotypes. In particular, the estimation of Zrs spectra at zero respiratory flow minimizes the contribution of upper airway nonlinearities and thus improves the assessment of intrapulmonary dynamics. However, as this study points out, most current multifrequency signals are suboptimal for exploiting the potential of IBOsc due to low signal-to-noise ratio and interaction between adjacent frequency components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".