Creatine Supplementation in Combat Sport Athletes: A Narrative Systematic Review
Bibliographic record
Abstract
Creatine is a widely studied ergogenic aid known for its effects on muscle performance and body composition. However, its impact or utility for athletes involved in combat sports, who often aim to reduce body mass to meet a specific weight class, remains unclear. To conduct a narrative systematic review of peer-reviewed published studies that examined the effects of creatine supplementation on body mass, body composition, muscular strength, power, endurance, fatigue, recovery, and cognitive performance in combat sport athletes. A comprehensive search was conducted in multiple public databases up to March 2025. Databases searched included PubMed, Scopus, Web of Science, and Google Scholar. Studies evaluating creatine supplementation in combat sports (eg wrestling, judo, taekwondo, boxing) were included. Outcomes assessed included changes in body mass and body composition, performance metrics, and markers of fatigue and recovery. Nineteen studies met the inclusion criteria. Creatine supplementation increased body mass, especially during longer-term interventions (≥6 wk) or when combined with structured resistance training. Creatine supplementation also increased measures of body mass and fat-free mass (FFM). Muscular power and maximal strength outcomes improved significantly after creatine supplementation, particularly in studies utilizing short-duration, high-intensity exercise protocols. Creatine supplementation did not influence measures of sport-specific endurance, recovery or fatigue. No serious adverse effects were reported across studies for creatine supplementation. Creatine supplementation enhances body mass, FFM, muscle strength and power in combat sport athletes. Given its safety and efficacy profile, creatine supplementation remains a promising supplement for supporting some aspects of athletic performance in combat sports.
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.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".