Cannabis and Athletic Performance
Bibliographic record
Abstract
Cannabis is widely used for both recreational and medicinal purposes on a global scale. There is accumulating interest in the use of cannabis and its constituents for athletic recovery, and in some instances, performance. Amidst speculation of potential beneficial applications, the effects of cannabis and its two most abundant constituents, delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), remain largely un-investigated. The purpose of this review is to critically evaluate the literature describing the effects of whole cannabis, THC, and CBD, on athletic performance and recovery. While investigations of whole cannabis and THC have generally shown either null or detrimental effects on exercise performance in strength and aerobic-type activities, studies of sufficient rigor and validity to conclusively declare ergogenic or ergolytic potential in athletes are lacking. The ability of cannabis and THC to perturb cardiovascular homeostasis warrants further investigation regarding mechanisms by which performance may be affected across different exercise modalities and energetic demands. In contrast to cannabis and THC, CBD has largely been scrutinized for its potential to aid in recovery. The beneficial effects of CBD on sleep quality, pain, and mild traumatic brain injury may be of particular interest to certain athletes. However, research in each of these respective areas has yet to be thoroughly investigated in athletic populations. Elucidating the effects of whole cannabis, THC, and CBD is pertinent for both researchers and practitioners given the widespread use of these products, and their potential to interact with athletes' performance and recovery.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".