Locomotor muscle dysfunction and rehabilitative exercise training in fibrotic interstitial lung disease: Where are we at and where could we go?
Bibliographic record
Abstract
Exercise limitation is a cardinal feature of fibrotic interstitial lung disease arising from pulmonary gas exchange, respiratory mechanical and cardio-circulatory abnormalities. More recently, it has been recognized that impairment in locomotor muscle function (e.g., reduced muscle mass/strength or heightened fatigability) might also play a relevant contributory role. Exercise training as part of pulmonary rehabilitation is the most effective intervention to improve exercise tolerance, dyspnoea and quality of life in patients with fibrotic interstitial lung disease. Given that exercise training has modest effects on exertional ventilation, breathing pattern and respiratory muscle performance, improvement in locomotor muscle function is a key target for pulmonary rehabilitation in these patients. In the present narrative review, we initially discuss whether the locomotor muscles of patients might be exposed to negative risk factors. After offering corroboratory evidence on this matter (e.g., oxidative stress, inflammation, hypoxia, physical inactivity and medications), we outline their effects on skeletal muscle mass and functional properties. We finish by addressing the potentially beneficial effects of rehabilitative exercise training on these muscle-centred outcomes, providing perspectives to facilitate or optimize the muscle benefits derived from this intervention. This narrative review, therefore, provides an up-to-date outline of the rationale for rehabilitative approaches focusing on the locomotor muscles in this patient population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".