Resistance Training Recommendations to Maximize Muscle Hypertrophy in an Athletic Population: Position Stand of the IUSCA
Bibliographic record
Abstract
Hypertrophy can be operationally defined as an increase in the axial cross-sectional area of a muscle fiber or whole muscle, and is due to increases in the size of pre-existing muscle fibers. Hypertrophy is a desired outcome in many sports. For some athletes, muscular bulk and, conceivably, the accompanying increase in strength/power, are desirable attributes for optimal performance. Moreover, bodybuilders and other physique athletes are judged in part on their muscular size, with placings predicated on the overall magnitude of lean mass. In some cases, even relatively small improvements in hypertrophy might be the difference between winning and losing in competition for these athletes. This position stand of leading experts in the field synthesizes the current body of research to provide guidelines for maximizing skeletal muscle hypertrophy in an athletic population. The recommendations represent a consensus of a consortium of experts in the field, based on the best available current evidence. Specific sections of the paper are devoted to elucidating the constructs of hypertrophy, reconciliation of acute vs long-term evidence, and the relationship between strength and hypertrophy to provide context to our recommendations.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.027 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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".