Common questions and misconceptions about creatine supplementation: what does the scientific evidence really show?
Bibliographic record
Abstract
Supplementing with creatine is very popular amongst athletes and exercising individuals for improving muscle mass, performance and recovery. Accumulating evidence also suggests that creatine supplementation produces a variety of beneficial effects in older and patient populations. Furthermore, evidence-based research shows that creatine supplementation is relatively well tolerated, especially at recommended dosages (i.e. 3-5 g/day or 0.1 g/kg of body mass/day). Although there are over 500 peer-refereed publications involving creatine supplementation, it is somewhat surprising that questions regarding the efficacy and safety of creatine still remain. These include, but are not limited to: 1. Does creatine lead to water retention? 2. Is creatine an anabolic steroid? 3. Does creatine cause kidney damage/renal dysfunction? 4. Does creatine cause hair loss / baldness? 5. Does creatine lead to dehydration and muscle cramping? 6. Is creatine harmful for children and adolescents? 7. Does creatine increase fat mass? 8. Is a creatine 'loading-phase' required? 9. Is creatine beneficial for older adults? 10. Is creatine only useful for resistance / power type activities? 11. Is creatine only effective for males? 12. Are other forms of creatine similar or superior to monohydrate and is creatine stable in solutions/beverages? To answer these questions, an internationally renowned team of research experts was formed to perform an evidence-based scientific evaluation of the literature regarding creatine supplementation.
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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 | 0.155 | 0.415 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".