Effectiveness assessment of oscillating positive expiratory pressure (OPEP) devices: Using a clinically relevant laboratory measure
Bibliographic record
Abstract
Rationale: OPEP devices are used for airway clearance where excess mucus is a challenge, such as in bronchiectasis, CF and COPD. The mechanism of device action can differ greatly between different devices and therefore a clinically relevant laboratory metric such as the one utilised in this study can provide additional insights into likely differences in effectiveness. Methods: Aerobika* (TMI), AirPhysio (AirPhysio), Flutter (Allergan), Acapella (ICU Medical) and RC Cornet Plus (Cegla) OPEP devices (n=3) were assessed at their highest resistance setting, utilising simulated OPEP expiratory breathing patterns at various different peak expiratory flows (PEFs), using a pressure wave generator. The total pressure pulse impact (TPPI), calculated as the sum of pressure pulse amplitudes for all discernable pulses (> 1.0 cm H2O) in a single exhalation, was determined for each pattern and each device. Results: see figure Discussion / Conclusions: The different mechanisms of OPEP device function appear to significantly impact the extent to which the pressure pulses are generated. The Aerobika* OPEP device had the largest TPPI values at all PEFs, while the two devices with metal ball mechanism had the lowest. Such differences highlight the risk of assuming that all devices will perform the same therapeutically and the importance of reviewing clinical efficacy and real-life usability.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".