Skeletal muscle atrophy in advanced interstitial lung disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: A limited number of studies examine skeletal muscle dysfunction in individuals with interstitial lung disease (ILD). We compared upper and lower limb muscle size and strength in individuals with advanced ILD with healthy controls. Second, the relationships of muscle size to muscle strength and function were explored. METHODS: Individuals with advanced ILD listed for lung transplant and healthy control subjects were studied. B-mode ultrasound was performed to assess cross-sectional area (CSA) of rectus femoris and thickness of gastrocnemius and soleus and biceps brachii. Subjects performed isometric muscle strength testing, Short Physical Performance Battery, Timed Up and Go, and Unsupported Upper Limb Exercise Test. RESULTS: Twenty-six individuals with advanced ILD (61 ± 8 years; 73% males; forced vital capacity: 2 ± 0.8 L, 49 ± 13% predicted; diffusing capacity of carbon monoxide: 9.3 ± 4 mL/min/mm Hg, 51 ± 20% predicted) and 12 healthy age and gender-matched controls (56 ± 9.5 years; 50% males) were included. Compared with controls, people with ILD had a smaller CSA of rectus femoris (7.6 ± 2.1 vs 9.4 ± 2.4 cm(2) ; P = 0.03) and lower strength of knee extensors (119 ± 35 vs 147 ± 39 Nm; P = 0.02) and plantarflexors (37 ± 19 vs 50 ± 15 Nm; P = 0.02), but not of biceps. Individuals with ILD also had impaired performance on all functional tests (P < 0.02). Moderate correlations were found between rectus femoris CSA and knee extensor strength (r = 0.63; P < 0.01) and biceps thickness and elbow flexor strength (r = 0.78; P < 0.01) in the ILD group. CONCLUSIONS: Individuals with advanced ILD presented with lower limb muscle atrophy and weakness. Future studies should evaluate the effectiveness of exercise training on muscle function in advanced ILD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".