Core Outcome Set for Studies Investigating Secretion Clearance Interventions Used in the Community by Patients With Neuromuscular Disease
Bibliographic record
Abstract
Background: Several treatments are used in the community to improve secretion clearance for patients with neuromuscular disease (NMD). However, the optimal intervention remains unclear with further research required. We aimed to develop a core outcome set (COS) for studies investigating secretion clearance interventions used in the community by patients with NMD. Methods: We conducted a scoping review, qualitative interviews with patients/family, modified e-Delphi survey, and consensus meeting. We recruited health care professionals, patients, and caregivers. Delphi participants were provided a 9-point Likert scale to score outcomes as “not important” (1– 3), “important but not critical” (4–6), or “critical” (7–9). Those scored as critical for inclusion were discussed at the consensus meeting using nominal group technique methods to achieve final consensus. Results: Ninety participants were recruited for the e-Delphi. Twenty-nine outcomes identified from the scoping review and qualitative interviews were taken forward to Round 1. Eleven additional outcomes were suggested by participants during Round 1. Forty outcomes were presented in Round 2. Sixteen outcomes were voted as critical for inclusion and taken forward to the consensus meeting (20 participants). The final COS includes measured cough strength/power, burden of respiratory illness, patient-reported effectiveness of secretion clearance, patient-reported experience of airway clearance, quality of life, adherence to secretion clearance intervention, and adverse events related to secretion clearance intervention. Conclusions: This COS should now be included in all trials investigating secretion clearance interventions in the community for patients with NMD. Next steps are to identify measurement tools and characteristics such as measurement time points for these outcomes using COSMIN methodology.
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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 | 0.211 | 0.313 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".