The Effects of Creatine Supplementation Combined with Resistance Training on Regional Measures of Muscle Hypertrophy: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
The purpose of this paper was to carry out a systematic review with a meta-analysis of randomized controlled trials that examined the combined effects of resistance training (RT) and creatine supplementation on regional changes in muscle mass, with direct imaging measures of hypertrophy. Moreover, we performed regression analyses to determine the potential influence of covariates. We included trials that had a duration of at least 6 weeks and examined the combined effects of creatine supplementation and RT on site-specific direct measures of hypertrophy (magnetic resonance imaging (MRI), computed tomography (CT), or ultrasound) in healthy adults. A total of 44 outcomes were analyzed across 10 studies that met the inclusion criteria. A univariate analysis of all the standardized outcomes showed a pooled mean estimate of 0.11 (95% Credible Interval (CrI): -0.02 to 0.25), providing evidence for a very small effect favoring creatine supplementation when combined with RT compared to RT and a placebo. Multivariate analyses found similar small benefits for the combination of creatine supplementation and RT on changes in the upper and lower body muscle thickness (0.10-0.16 cm). Analyses of the moderating effects indicated a small superior benefit for creatine supplementation in younger compared to older adults (0.17 (95%CrI: -0.09 to 0.45)). In conclusion, the results suggest that creatine supplementation combined with RT promotes a small increase in the direct measures of skeletal muscle hypertrophy in both the upper and lower body.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".