Cannabis and the Health and Performance of the Elite Athlete
Bibliographic record
Abstract
OBJECTIVE: Cannabis (marijuana) is undergoing extensive regulatory review in many global jurisdictions for medical and nonmedical access. Cannabis has potential impact on the health of athletes as well as on performance in both training and in competition. The aim of this general review is to identify and highlight the challenges in interpreting information with respect to elite athletic performance, and to point to important research areas that need to be addressed. DATA SOURCES: A nonsystematic literature review was conducted using Medline and PubMed for articles related to cannabis/marijuana use and sports/athletic performance; abstracts were reviewed by lead author and key themes identified and explored. MAIN RESULTS: Cannabis may be primarily inhaled or ingested orally for a range of medical and nonmedical reasons; evidence for efficacy is limited but promising for chronic pain management. Although evidence for serious harms from cannabis use on health of athletes is limited, one should be cognizant of the potential for abuse and mental health issues. Although the prevalence of cannabis use among elite athletes is not well-known, use is associated with certain high-risk sports. There is no evidence for cannabis use as a performance-enhancing drug. CONCLUSIONS: Medical and nonmedical cannabis use among athletes reflects changing societal and cultural norms and experiences. Although cannabis use is more prevalent in some athletes engaged in high-risk sports, there is no direct evidence of performance-enhancing effects in athletes. The potential beneficial effects of cannabis as part of a pain management protocol, including reducing concussion-related symptoms, deserve further attention.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".