What can metabolic myopathies teach us about exercise physiology?
Bibliographic record
Abstract
Exercise physiologists are interested in metabolic myopathies because they demonstrate how knocking out a component of a specific biochemical pathway can alter cellular metabolism. McArdle's disease (myophosphorylase deficiency) has often been studied in exercise physiology to demonstrate the influence of removing the major anaerobic energy supply to skeletal muscle. Studies of patients with McArdle's disease have shown the increased reliance on blood-borne fuels, the importance of glycogen to maximal aerobic capacity, and the use of nutritional strategies to bypass metabolic defects. Myoadenylate deaminase deficiency is the most common metabolic enzyme deficiency in human skeletal muscle. It is usually compensated for endogenously and does not have a major influence on high-energy power output. Nutritional interventions such as carbohydrate loading and carbohydrate supplementation during exercise are essential components of therapy for patients with fatty acid oxidation defects. Cases of mitochondrial myopathies illustrate the importance of peripheral oxygen extraction for maximal aerobic capacity and show how both exercise and nutritional interventions can partially compensate for these mutations. In summary, metabolic myopathies provide important insights into regulatory and nutritional aspects of the major biochemical pathways of intermediary metabolism in human skeletal muscle.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".