Common questions and misconceptions about creatine supplementation: what does the scientific evidence really show?
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| 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