How should we measure arm exercise capacity in COPD? A systematic review
Bibliographic record
Abstract
Background: There are no recommendations on how to measure arm exercise capacity in individuals with chronic obstructive pulmonary disease (COPD). The objectives of this study were to: (i) synthesize the literature on measures of arm exercise capacity in individuals with COPD; (ii) describe the psychometric properties and the target construct of each measure and (iii) make recommendations for clinical practice and research. Methods: Studies conducted in COPD that included a measure of arm exercise capacity were identified after searches of 5 electronic databases (MEDLINE, CINAHL, EMBASE, Physiotherapy Evidence Database and Cochrane Library) and reference lists of pertinent articles. One reviewer performed data extraction and two assessed quality of studies that described measurement properties using the Consensus-based standards for the selection of health measurement instrument. Results: Of 654 reports, 41 met the study criteria. Five types of arm exercise tests were indentified: arm ergometry, ring shifts, dowel lifts, proprioceptive neuromuscular facilitation, and activities of daily living (ADL) tests. Four studies assessed measurement properties of the Unsupported Upper Limb Exercise test (UULEX), 6-minute Pegboard and Ring test (6PBRT), a test involving weight shifts and the Grocery Shelving Task (GST). Validity studies were of fair to good quality, whereas reliability studies were of poor quality. Conclusions: Arm ergometry may be best for measuring peak arm exercise capacity and endurance during supported exercises, while the UULEX, 6PBRT and GST may better reflect ADL and should be the tests of choice to measure peak unsupported arm exercise capacity (UULEX) and arm function (6PBRT and GST).
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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 | 0.029 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".