Assessment of a new pressure manometer for use with an oscillating positive expiratory pressure (OPEP) device
Bibliographic record
Abstract
Rationale: The positive expiratory pressure generated from OPEP airway clearance devices is routinely targeted between 10 and 20cmH2O and can be confirmed through the use of a manometer attached to the OPEP device. This investigation assessed a new pressure manometer and determined how the control of pressure might also influence the frequency of oscillations. Methods: The pressure manometer (TMI, Canada) was assessed with the Aerobika* OPEP device (TMI). The manometer is attached directly to this OPEP device in the line of site of the user. Seven healthy volunteers were instructed to exhale through the OPEP device (3 times) according to the instructions for use and the average frequency of all oscillations per breath calculated for each subject. The same volunteers repeated the exercise with the manometer attached to the OPEP device and instruction to target the middle of the desired pressure range on the manometer. Results: See figure below. A theoretical optimum frequency range of 12-15Hz (King et al, 1983; Silva et al, 2009) is shaded. Conclusions: Use of the OPEP manometer in this study provided evidence supporting not only the utility with respect to ensuring a safe and effective positive pressure during use of the OPEP device, but also the ability to focus the related oscillation frequencies more closely in alignment with the reported optimum Hz range.
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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 | 0.010 |
| 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 | 0.000 |
| 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".