Part II. Common questions and misconceptions about creatine supplementation: what does the scientific evidence really show?
Bibliographic record
Abstract
Creatine monohydrate supplementation (CrM) is a safe and effective intervention for improving certain aspects of sport, exercise performance, and health across the lifespan. Despite its evidence-based pedigree, several questions and misconceptions about CrM remain. To initially address some of these concerns, our group published a narrative review in 2021 discussing the scientific evidence as to whether CrM leads to water retention and fat accumulation, is a steroid, causes hair loss, dehydration or muscle cramping, adversely affects renal and liver function, and if CrM is safe and/or effective for children, adolescents, biological females, and older adults. As a follow-up, the purpose of this paper is to evaluate additional questions and misconceptions about CrM. These include but are not limited to: 1. Can CrM provide muscle benefits without exercise? 2. Does the timing of CrM really matter? 3. Does the addition of other compounds with CrM enhance its effectiveness? 4. Does CrM and caffeine oppose each other? 5. Does CrM increase the rates of muscle protein synthesis or breakdown? 6. Is CrM an anti-inflammatory intervention? 7. Can CrM increase recovery following injury, surgery, and/or immobilization? 8. Does CrM cause cancer? 9. Will CrM increase urine production? 10. Does CrM influence blood pressure? 11. Is CrM safe to consume during pregnancy? 12. Does CrM enhance performance in adolescents? 13. Does CrM adversely affect male fertility? 14. Does the brain require a higher dose of CrM than skeletal muscle? 15. Can CrM attenuate symptoms of sleep deprivation? 16. Will CrM reduce the severity of and/or improve recovery from traumatic brain injury? Similar to our 2021 paper, an international team of creatine research experts was formed to perform a narrative review of the literature regarding CrM to formulate evidence-based responses to the aforementioned misconceptions involving CrM.
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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 | 0.105 | 0.333 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".