Bibliographic record
Abstract
BACKGROUND/AIMS: Caffeine and creatine are 2 of the most widely available and used compounds in sport. Although the use of either is not considered a doping infraction, the evidence does suggest ergogenic potential in certain sports. The purpose of this paper is to review the pharmacology and potential mechanism(s) of action of caffeine and creatine as they pertain to possible use as an ergogenic aid in sport. METHODS: Previous review articles on caffeine and creatine use in sport were screened for relevant information and references, and studies for review and recent articles (2007 onwards) were obtained and reviewed using a PUBMED search with the terms 'caffeine AND exercise', 'creatine and creatine monohydrate AND exercise', and appropriate linked articles were evaluated. RESULTS: Caffeine taken before (3-6 mg/kg) or during (1-2 mg/kg) endurance exercise enhances performance, through central nervous system and direct muscle effects. Creatine monohydrate supplementation at higher (approx. 20 g/day × 3-5 days) or lower (approx. 5 g/day × 30 days) doses increases skeletal muscle total and phosphocreatine by 10-20%. Creatine supplementation appears to minimally but significantly enhance high-intensity sport performance and the mass and possibly strength gains made during resistance exercise training over the first few months. CONCLUSIONS: Although caffeine and creatine appear to be ergogenic aids, they do so in a sport-specific context and there is no rationale for their simultaneous use in sport. Higher doses of caffeine can be toxic and appear to be ergolytic. There is no rationale for creatine doses in excess of the recommendations, and some athletes can get stomach upset, especially at higher creatine doses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".