Effects of high-velocity resistance training and creatine supplementation in untrained healthy aging males
Bibliographic record
Abstract
The purpose was to investigate the effects of high-velocity resistance training (HVRT) and creatine supplementation in untrained healthy aging males. Participants were randomized to supplement with creatine (0.1 g·kg −1 ·day −1 of creatine + 0.1 g·kg −1 ·day −1 of maltodextrin) or placebo (0.2 g·kg −1 ·day −1 of maltodextrin) during 8 weeks of HVRT. Prior to and following HVRT and supplementation, assessments were made for muscle strength, muscle thickness, peak torque, and physical performance. There was a significant increase over time for all measures of muscle strength (p < 0.001), muscle thickness (p < 0.001), and some measures of peak torque (knee flexion; 1.05 and 3.14 rad/s; p < 0.001) and physical performance (balance board time-to-completion; p = 0.017). There was a group × time interaction for leg press strength (p = 0.044) and total lower-body strength (leg press, knee flexion, knee extension combined; p = 0.039). The creatine group experienced greater gains in leg press and total lower-body strength compared with the placebo group, with no other differences. HVRT increases muscle strength, muscle thickness, and some measures of peak torque and physical performance in untrained healthy aging males. The addition of creatine supplementation to HVRT further augments the gains in leg press and total lower-body strength. Novelty High-velocity resistance training increases muscle mass and performance. Creatine supplementation increases lower-body muscle strength. High-velocity resistance training and creatine supplementation are safe interventions for aging adults.
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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 | 0.000 | 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) | 0.002 | 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 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".