Relationship between fatigue, physical activity and health-related factors in COPD
Bibliographic record
Abstract
Fatigue is highly prevalent in COPD and may be associated with reduced physical activity (PA) and poor outcomes. This study explored the relationship between fatigue, objectively measured PA and health-related factors in people with COPD. Fatigue was assessed with the Checklist of Individual Strength (CIS20) and CIS20-Subjective Fatigue (CIS20-SF) and PA with Actigraph GT3X monitors (moderate-to-vigorous PA, MVPA; total PA; steps/day). Dyspnoea (modified Medical Research Council, mMRC), exercise tolerance (6-min walk distance, 6MWD), lung function (spirometry) and GOLD A-D were collected. Spearman (ρ) and Pearson (r) correlations and multiple regressions were performed. Variables entered the model if correlation≥0.2. 54 patients participated (68±7 years; 82% men) and 69% reported fatigue (CIS20-SF≥27). Fatigue was significantly correlated with MVPA, steps/day, mMRC, 6MWD, GOLD A-D and FEV1pp (Table 1). In regression models for CIS20 (p=.001; r2=.61) and CIS20-SF (p=.003; r2=.56), dyspnoea was the only significant variable. Table 1. Descriptives and correlations between fatigue, PA and health-related factors. aρ; br; cVariables entering the regression models; dMedian[Q1-Q3], mean±SD or n. People with higher scores of fatigue present lower PA levels, although the relationship is weak. Dyspnoea appears to have the largest influence on fatigue.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".