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Record W4410017402 · doi:10.1016/j.cmpb.2025.108818

Tracking of respiratory mechanics at multiple oscillation frequencies

2025· article· en· W4410017402 on OpenAlexaff
Gergely Makan, Joyce Wu, Chung‐Wai Chow, Yushu Zou, Ronald J. Dandurand, Dorottya Czövek, Zoltán Gingl, Zoltán Hantos

Bibliographic record

VenueComputer Methods and Programs in Biomedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsMcGill University Health CentreUniversity Health Network
Fundersnot available
KeywordsRespiratory physiologyOscillation (cell signaling)Computer scienceTracking (education)MechanicsRespiratory systemAcousticsPhysicsStatistical physicsMedicineInternal medicineChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.078
GPT teacher head0.387
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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