Creatine supplementation and resistance training: a comparison between novice and experienced lifters - a systematic review and dose-response meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it