Laboratory investigation into the effect of flow rate on the performance of four oscillating positive expiratory pressure (OPEP) devices: Does mechanism of action matter and considerations for clinical relevance
Bibliographic record
Abstract
INTRODUCTION: OPEP devices are used therapeutically to aid airway clearance where excess mucus is a challenge. OPEP devices often have differing mechanisms of action. This laboratory study compared four different OPEP devices, each with a distinctly different mechanism of action in producing the OPEP. Methods: Aerobika* (TMI), Acapella Choice Blue (ICU Medical), GeloMuc (Pohl Boskamp), and RC Cornet Plus (Cegla) OPEP devices (n=3) were assessed at steady expiratory flows of 10-40L/min using a flow generator (Resmed VPAP III), flow meter (TSI 4000), pressure tap and computer for data analysis. Average positive pressure, pressure pulse amplitude and pulse frequency were determined for each device. RESULTS / DISCUSSION: Each device can be operated at different resistances. The values at a medium resistance are reported in this study as this is typically the recommended starting setting. erj;64/suppl_68/PA1368/F1 F1 F1 Conclusions: Each OPEP device operates differently mechanically. This may impact device performance and potentially the clinical benefit of the device. In this study the Aerobika* OPEP device performed the best overall. When selecting an OPEP device for a patient, the existence of clinical evidence supporting efficacy, as well as lab data and patient preference, should be considered. All devices will not perform the same.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".