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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".