Assessing the accuracy of oscillometry in tracking the mean values and the temporal changes in impedance of children
Bibliographic record
Abstract
Introduction: Within-breath analysis of respiratory mechanics – resistance (Rrs) and reactance (Xrs) – provides important information of the health of the respiratory system, but little work has been done estimating the accuracy of tracking the temporal changes in impedance. The accuracy of current oscillometry techniques can only be assessed via computational models. Materials &Methods: Here we modeled the respiratory system as a single-compartment lung model with time-varying Rrs and Xrs for obstructed asthma according to measurements of mechanics and breathing noise from 7 children (4m/3f, ages 7 to 12) with asthma specifically chosen to span a range of breathing frequencies (0.25 to 0.5 Hz) using an airwave oscillometry device (tremoFlo™). We also extended the theory to include the temporal changes in the model, which helps compute an estimate of the temporal tracking error (TTE). Results: Accuracy for mean Rrs and Xrs exceeded 99% for all conditions. TTE increased from 3.1 ± 1.2 % to 9.9 ± 2.3% with increasing breathing rate up to 0.43 Hz independent from noise amplitude, and only exceeded 10% of the mean Rrs, at the highest breathing frequency (15.3% ± 3.1% at 0.5 Hz). Coherence remained at 0.92 ± 0.02 at all frequencies. Conclusion: Results indicate that coherence is not a good measure of data quality for within-breath tracking of impedance. Moreover, while mean breathing frequency can increase errors, this does not greatly affect mean Rrs, thus the results further recommend training subjects to reduce breathing rate to accurately track temporal variation in respiratory mechanics.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".