Is inflammation good, bad or irrelevant for skeletal muscles in COPD?
Bibliographic record
Abstract
Skeletal muscle weakness is a common and serious finding in patients with advanced chronic obstructive pulmonary disease (COPD) and contributes to their morbidity and mortality, increasing the risk of exacerbations, hospitalisations and death by 3–4-fold.1 2 The treatment for muscle dysfunction of COPD is extremely limited, and the multitude of interventions to address poor muscle performance has not been fully explored in these patients. Over the past decade, there has been an explosion of interest and research on this topic. Despite that, the pathophysiological mechanisms linking the lung disease of COPD with skeletal muscle dysfunction remains largely unknown. Identifying a link between lung disease and muscle performance might indeed be a daunting task because of the influence of other comorbid conditions (and medications), previous musculoskeletal injury and the history of physical activity that might influence the current status of skeletal muscle in this condition. There are, however, several observations that are widely known and accepted. Firstly, skeletal muscle weakness increases with progression of disease. Secondly, histologically, biopsies of large limb muscles consistently demonstrate a reduction in muscle mass, especially of the anaerobic type-IIx fibres, a shift of fibre type from type 1 fibres to a predominance of type 2 fibres and the depletion of mitochondrial oxidative enzymes leading to uncoupling of oxidative phosphorylation and reduced aerobic capacity.3 Interestingly, these muscles also demonstrate increased oxidative stress and accelerated …
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.021 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".