Airway clearance techniques for people with acute exacerbation of COPD: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Acute exacerbations of COPD (AECOPD) often involve mucus hypersecretion. Thus, management of sputum retention is critical. However, the use of airway clearance techniques (ACTs) in people with AECOPD across different healthcare settings and factors influencing their selection remain unclear. OBJECTIVE: To identify and map ACTs used for AECOPD in different healthcare settings and the factors influencing clinical decision-making worldwide. METHODS: Four electronic databases and grey literature were searched from 1995 to December 2023, with hand-searching of eligible records. The Joanna Briggs Institute methodology for scoping reviews was followed. RESULTS: 25 articles were included: 14 clinical studies, five guidelines/statements and six surveys/audits. Clinical studies reported the use of a wide range of single or combined ACTs, with no clear pattern in using particular ACTs in different parts of the world. Recent guidelines advise using ACTs for certain patients with AECOPD, particularly those with hypersecretion, with most guidelines recommending positive expiratory pressure (PEP) therapy. According to surveys, the most used ACTs in Australia and Europe are active cycle of breathing techniques, PEP or forced expiratory technique, while vibrations are most frequently used in Canada. Factors influencing the selection of specific ACTs include the presence of contraindications, level of dyspnoea, access to resources/equipment and ease of learning/performing the technique. All information was derived from hospital settings. CONCLUSIONS: This scoping review identified and mapped ACTs used for people with AECOPD worldwide and their decision-making factors. Future work should focus on community settings.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".