Investigating human skeletal muscle physiology with unilateral exercise models: when one limb is more powerful than two
Bibliographic record
Abstract
Direct sampling of human skeletal muscle using the needle biopsy technique can facilitate insight into the biochemical and histological responses resulting from changes in exercise or feeding. However, the muscle biopsy procedure is invasive, and analyses are often expensive, which places pragmatic restraints on sample sizes. The unilateral exercise model can serve to increase statistical power and reduce the time and cost of a study. With this approach, 2 limbs of a participant are randomized to 1 of 2 treatments that can be applied almost concurrently or sequentially depending on the nature of the intervention. Similar to a typical repeated measures design, comparisons are made within participants, which increases statistical power by reducing the amount of between-person variability. A washout period is often unnecessary, reducing the time needed to complete the experiment and the influence of potential confounding variables such as habitual diet, activity, and sleep. Variations of the unilateral exercise model have been employed to investigate the influence of exercise, diet, and the interaction between the 2, on a wide range of variables including mitochondrial content, capillary density, and skeletal muscle hypertrophy. Like any model, unilateral exercise has some limitations: it cannot be used to study variables that potentially transfer across limbs, and it is generally limited to exercises that can be performed in pairs of treatments. Where appropriate, however, the unilateral exercise model can yield robust, well-controlled investigations of skeletal muscle responses to a wide range of interventions and conditions including exercise, dietary manipulation, and disuse or immobilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".