Resistance exercise and nutrition to counteract muscle wasting
Bibliographic record
Abstract
Loss of muscle mass is an unfavourable consequence of aging and many chronic diseases. The debilitating effects of muscle loss include declines in physical function and quality of life and increases in morbidity and mortality. Loss of muscle mass is the result of a decrease in muscle protein synthesis, an increase in muscle protein degradation, or a combination of both. Much research on muscle wasting has tended to focus on preventing muscle protein breakdown, and less attention has been paid to providing adequate stimulation to increase muscle protein synthesis. In this review, we present evidence to suggest that interventions aimed at increasing muscle protein synthesis represent the most effective countermeasure for preventing, delaying, or reversing the loss of skeletal muscle mass experienced in various muscle wasting conditions. Based on results from acute and chronic studies in humans in a wide variety of wasting conditions, we propose that resistance exercise training combined with appropriately timed protein (likely leucine-rich) ingestion represents a highly effective means to promote muscle hypertrophy, and may represent a highly effective treatment strategy to counteract the muscle wasting tassociated with aging and chronic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".