Effects of Positive End‐Expiratory Pressure on Oscillated Volume during High‐Frequency Chest Compression in Children with Cystic Fibrosis
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of positive end-expiratory pressure (PEEP) on end-expiratory lung volume (EELV) and mean oscillated volume (V(osc)) during high frequency chest compression (HFCC). DESIGN: A clinic-based prospective intervention study. SETTING: Pulmonary function laboratory, University of Alberta, Edmonton, Alberta. POPULATION: Nine children with cystic fibrosis with little or no obstructive airway disease who were selected from the outpatient Cystic Fibrosis and Pediatric Pulmonary Clinics at the University of Alberta Hospital, Edmonton, Alberta. METHODS: Each child received HFCC alone (at 10 Hz with chest wall pressure of 8 cm H2O) and HFCC plus PEEP. A closed circuit spirometry system was used to measure HFCC- and PEEP-induced changes in EELV, expressed as per cent baseline functional residual capacity (FRC) measured using helium dilution. An isothermic chamber permitted measurement of V(osc). RESULTS: HFCC caused a significant 9% decrease in EELV. Adding 2.0 +/- 0.3 cm H2O of PEEP increased EELV back to at least the FRC level. With HFCC alone, Vosc was significantly lower during spontaneous expiration than during spontaneous inspiration, but adding PEEP to HFCC increased V(osc), especially during spontaneous expiration. CONCLUSIONS: Adding PEEP during HFCC prevents the decrease in EELV and increases V(osc). Therefore, PEEP may improve HFCC-induced mucus clearance in children with cystic fibrosis.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".