Airway clearance in bronchiectasis: a randomized crossover trial of active cycle of breathing techniques versus Acapella
Bibliographic record
Abstract
BACKGROUND: The efficacy of a new airway clearance device (Acapella) has not been previously investigated. Active cycle of breathing techniques (ACBT) is the standard airway clearance technique used in patients with bronchiectasis. OBJECTIVE: The objective of this study was to compare the efficacy of ACBT with Acapella as methods of airway clearance in adults with stable, productive bronchiectasis. METHODS: Twenty patients (7 males), age 58 +/- 11 years (mean +/- SD), FEV1 64 +/- 22% predicted with stable (change of not greater than FEV1 10% predicted during 3 months prior to study), productive (history of expectoration of half an egg cup sputum/day) bronchiectasis attended the respiratory clinic on 3 days. Day 1: 40-min training session on ACBT and Acapella. Days 2 and 3: 30-min treatment session of either ACBT or Acapella. Treatment order was determined by a concealed randomization procedure. The following outcomes were measured before and after treatment spirometry, SpO2 and breathlessness by an independent assessor who was blinded to treatment order. Weight of sputum (during treatment plus 30 min after treatment), number of coughs and patient preference were also recorded. RESULTS: No significant differences were found at baseline indicating that patients were stable. No significant differences were found between weight of sputum expectorated with ACBT treatment and weight of sputum expectorated with Acapella treatment--mean difference 0.54 g (95% CI -0.39 to 1.46). A greater proportion of patients preferred Acapella (14/20). CONCLUSION: Acapella is as effective a method of airway clearance as ACBT and may offer a user-friendly alternative to ACBT for patients with bronchiectasis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".