S102 Assessment of two Oscillating Positive Expiratory Pressure (OPEP) devices: how do the differing mechanisms of action impact lab performance
Bibliographic record
Abstract
Rationale OPEP devices are often used therapeutically in order to aid airway clearance where excess mucus is a challenge, such as in bronchiectasis, CF and COPD. Ease of use, ability to clean and adaptability to use with nebulizers are real world differentiators for different types of OPEP device, however the mechanism of device action can also differ. This laboratory study compared an established, clinically supported OPEP device with a recently introduced one that is based on older technology. Key in-vitro performance parameters were compared. Methods Aerobika* (Trudell Medical International, Canada) and AirPhysio (AirPhysio, Australia) OPEP devices (n=3) were assessed at steady expiratory flows of 10–30L/min using a flow generator (Resmed VPAP III), flow meter (TSI 4000), pressure tap and computer for data collection and analysis. Average positive pressure, pulse amplitude and pulse frequency were determined for each device. Results As each device can be operated at different resistances, the values at medium resistance are reported in figure 1. Discussion/Conclusions For effective performance, frequency is typically desired to be in the 10–15 Hz range, mean pressure ideally between 10–20 cm H2O, and pulse amplitude as large as possible. The results for the two devices show that although mean pressures are similar across the range of flow rates, the amplitudes are higher for the Aerobika* OPEP device and the frequencies are more often in the desired range. The observed differences are probably due to the fact that each device operates according to a different mechanical principle. What is clear from these results is that, in addition to real world usability assessments, it is important to understand that each OPEP device can perform differently mechanically. Hence, when selecting an OPEP device for a patient, the existence of clinical evidence supporting efficacy, as well as patient preference, should be considered. All devices will not perform the same. Please refer to page A211 for declarations of interest related to this abstract.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".