Outcomes and measures in studies of techniques to promote secretion clearance in individuals with neuromuscular conditions: a scoping review
Bibliographic record
Abstract
Background: Techniques to support secretion clearance for individuals with neuromuscular conditions and respiratory muscle weakness include mechanical insufflation-exsufflation and chest wall vibrations. Assessing the comparative efficacy of these techniques is challenging due to the absence of a core outcome set. We sought to describe outcomes and measurement instruments reported in studies of airway clearance techniques for individuals with neuromuscular conditions living in the community. Methods: We conducted a scoping review of primary research studies. We searched six databases from inception to 22 February 2024. Two reviewers independently screened citations against the inclusion criteria and extracted data on outcomes and measurement characteristics. Outcomes were categorised according to the Core Outcome Measures in Effectiveness Trials (COMET) 38-domain taxonomy. Results: We identified 75 eligible studies describing 55 outcomes. We grouped outcomes deemed overlapping and categorised them using the COMET 38-domain taxonomy, resulting in 34 distinct outcomes. Common physiological/clinical outcomes were cough strength (n=48 studies, 64%), lung volume (n=48, 64%) and insufflation capacity (n=22, 29%). The most common measurement tools for these outcomes were spirometer (n=38, 51%), peak flow meter (n=24, 32%) and pneumotachograph (n=20, 27%). The most common resource-use outcome was hospitalisation due to respiratory illness (n=13, 17%). Few studies reported on life impact outcomes, with the most common being comfort (n=6, 8%) and patient satisfaction (n=4, 5%). Conclusion: We identified 34 outcomes from 75 studies, which were most commonly physiological/clinical, with resource-use and life impact outcomes being seldom reported. The number and range of outcomes and measures demonstrates the need for a core outcome set.
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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 | 0.063 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.034 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".