Variability in expiratory flow requirements among oscillatory positive expiratory pressure devices
Bibliographic record
Abstract
Introduction: Oscillatory positive expiratory pressure (OPEP) devices facilitate secretion clearance by generating positive end expiratory pressure.However, different device designs may produce different levels of expiratory pressure with the same expiratory flow rate.We bench tested four devices to determine the relationship between expiratory flow and expiratory pressure in each.Methods: A bench model was created to test the gas flow rates required by different OPEP devices to generate target expiratory pressure.Four different devices were tested: Acapella ® (DH Green, Smiths Medical), AerobiKa ® (Monaghan Medical Corporation), VibraPEP ® (Curaplex), and vPEP™ (D R Burton Healthcare).Each OPEP device was tested to determine the expiratory flow needed to generate expiratory pressure thresholds considered appropriate for OPEP therapy.Results: The expiratory flow required to generate the same expiratory pressure thresholds varied considerably among devices.Valved OPEP devices such as the VibraPEP required less flow than mechanical devices such as the vPEP, Aerobika, and Acapella.Discussion: In this bench test of OPEP devices, we found considerable variability in expiratory flow requirements needed to generate an expiratory pressure of >10 cm H 2 O.Our finding suggests that smaller patients or those with limited expiratory airflow due to diseases such as COPD, obesity, chronic congestive heart failure, and restrictive lung disease may have better results when matched to OPEP devices requiring less expiratory airflow.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".