<p>A Functional Respiratory Imaging Approach to the Effect of an Oscillating Positive Expiratory Pressure Device in Chronic Obstructive Pulmonary Disease</p>
Bibliographic record
Abstract
Purpose: Chronic obstructive pulmonary disease (COPD) patients are prone to suffer from chronic bronchitis, which ultimately affects their quality of life and overall prognosis. Oscillating positive expiratory pressure (oPEP) devices are designed to aid in the mucus clearance by generating positive pressure pulses in the airways. The main aim of this study was to analyze the impact of a specific oPEP device – Aerobika ® – on top of standard of care medication in COPD patients’ lung dynamics and drug deposition. Patients and Methods: In this single-arm pilot study, patients were assessed using standard spirometry tests and functional respiratory imaging (FRI) before and after a period of 15± 3 days of using the oPEP device twice daily (before their standard medication). Results: The utilization of the oPEP device led to a significant increase of 2.88% in specific airway volume after two weeks (1.44 (SE: 0.18) vs 1.48 (SE: 0.19); 95% CI = [0.03%,5.81%]; p=0.048). Moreover, the internal airflow distribution (IAD) was affected by the treatment: patients’ changes ranged from − 6.74% to 4.51%. Furthermore, IAD changes at the lower lobes were also directly correlated with variations in forced expiratory volume in one second and peak expiratory flow; conversely, IAD changes at the upper lobes were inversely correlated with these clinical parameters. Interestingly, this change in IAD was significantly correlated with changes in lobar drug deposition ( r 2 =0.30, p< 0.001). Conclusion: Our results support that the Aerobika device utilization leads to an improved airflow, which in turn causes a shift in IAD and impacts the drug deposition patterns of the concomitant medication in patients with COPD. Keywords: mucus hypersecretion, airway clearance techniques, functional respiratory imaging, internal airflow distribution, drug deposition
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".