Creatine supplementation and resistance training: a comparison between novice and experienced lifters - a systematic review and dose-response meta-analysis
Bibliographic record
Abstract
BACKGROUND: Creatine (Cr) supplementation is well established for enhancing fat-free mass (FFM) when combined with resistance training (RT). However, the influence of prior training experience on supplementation efficacy remains unknown. OBJECTIVE: This systematic review and dose-response meta-analysis of controlled trials evaluated the effects of Cr supplementation combined with RT on body composition, with particular emphasis on the differences between trained (experienced) and untrained (novice) individuals. METHODS: A systematic search of major databases was conducted to identify controlled trials published until March 2025. The effects of Cr supplementation on body mass, body mass index (BMI), FFM, fat mass (FM), and body fat percentage (BFP) were examined using random-effects meta-analysis. RESULTS: < 0.001) without significant effects on FM, BMI, and BFP. Trained individuals exhibited greater, though non-significant, gains in FFM (1.82 vs. 1.23 kg) compared with untrained participants, despite similar increases in total body mass. Dose-response analyses identified significant relationships between Cr dose and changes in body mass and BMI. Furthermore, supplementation duration was associated with changes in BFP and body mass. CONCLUSION: Both novice and experienced lifters gained FFM with Cr supplementation compared to placebo. The increase in FFM was approximately 0.6 kg (≈50%) greater in experienced participants; however, this between-group difference was not statistically significant.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".