The Flutter Device and Expiratory Pressures
Bibliographic record
Abstract
PURPOSE: Flutter therapy uses a handheld instrument that consists of a pipe-like device with a ball in the central core that oscillates during exhalation, providing oscillating positive expiratory pressure. The purpose of this study was to determine the effect of airflow and the incline of the device at the mouth on expiratory pressure and oscillation frequency. METHODS: A Flutter device was attached to a circuit that consisted of a pneumotachograph and a ventilator. The ventilator generated different flows and expiratory pressure was measured with a pressure transducer. The angles considered were +40 degrees to -40 degrees in increments of 10 degrees , with the reference for incline being the horizontal line. Expiratory pressure, airflow, angle of incline, and oscillation frequency were measured. RESULTS: There was a strong and significant correlation between flow and expiratory pressure at each level of incline (P < or =006; r > 0.93). There also was a significant and strong correlation between expiratory pressure and oscillation frequency (P <.05; r = 0.81-0.97). There was a significant reduction in expiratory pressure at a negative incline of -40 degrees. CONCLUSIONS: The results of this study indicate that a positive incline and a large airflow result in an increase in expiratory pressure. This information will assist clinicians to better understand the effects of the Flutter device.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".