Effects of Changes in Lung Volume on Oscillatory Flow Rate During High‐Frequency Chest Wall Oscillation
Bibliographic record
Abstract
BACKGROUND: The effectiveness of high-frequency chest wall oscillation (HFCWO) in mucolysis and mucous clearance is thought to be dependant on oscillatory flow rate (Fosc). Therefore, increasing Fosc during HFCWO may have a clinical benefit. OBJECTIVES: To examine effects of continuous positive airway pressure (CPAP) on Fosc at two oscillation frequencies in healthy subjects and patients with airway obstruction. METHODS: Five healthy subjects and six patients with airway obstruction underwent 12 randomized trials of HFCWO (CPAP levels of 0 cm H2O, 2 cm H2O, 4 cm H2O, 6 cm H2O, 8 cm H2O and 10 cm H2O at frequencies of 10 Hz and 15 Hz) within a body plethysmograph, allowing measurements of changes in lung volume. Fosc was measured by reverse plethysmography using a 20 L isothermic chamber near the mouth. At the end of each randomized trial, an inspiratory capacity manoeuvre was used to determine end-expiratory lung volume (EELV). RESULTS: EELV increased significantly (P<0.05) with each level of CPAP regardless of oscillation frequency. Fosc also significantly increased with CPAP (P<0.05) and it was correlated with EELV (r=0.7935, P<0.05) in obstructed patients but not in healthy subjects (r=0.125, P=0.343). There were no significant differences in perceived comfort across the levels of CPAP. CONCLUSIONS: Significant increases in Fosc with CPAP-induced increases in lung volume were observed, suggesting that CPAP may be useful as a therapeutic adjunct in patients who have obstructive airway disease and who require HFCWO.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".