Myostatin inhibition: a potential performance enhancement strategy?
Bibliographic record
Abstract
A decade has passed since myostatin was first identified as a negative regulator of muscle growth. Since then, studies in both humans and animals have demonstrated that decreasing the levels of this growth factor or inhibiting its function can dramatically increase muscle size, and a number of therapeutic applications of myostatin inhibition to the treatment of myopathies and muscle atrophy have been proposed. As such treatments would be likely to also stimulate muscle growth in healthy individuals, there is a growing concern among anti-doping authorities that myostatin inhibitors may be among the next generation of ergogenic pharmaceuticals or even in the vanguard of "gene doping" technology. While the ability to stimulate muscle growth through myostatin inhibition is well documented, a growing body of evidence suggests such increases may not translate into an improvement in athletic performance. This article briefly reviews the function of this potent regulator of muscle development and explores the potential therapeutic uses, and potential ergogenic abuses, of myostatin manipulation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".