Marijuana and Its Effects on Athletic Performance: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of marijuana on athletic performance. DESIGN: We searched MEDLINE, EMBASE, CINAHL, PsycINFO, AMED, and SPORTDiscus from their beginning to September 2016. Study quality was assessed using the Cochrane collaboration tool for assessing risk of bias and the Cochrane GRADE scale. No meta-analyses were performed for this review. SETTING: Subjects in a track, gym, or recreational ward. PARTICIPANTS: Any primary study which included male and female adults of any athletic background between ages 18 and 65, with no other comorbid conditions. INTERVENTIONS: Any primary study which used marijuana cigarettes and included a control group. MAIN OUTCOME MEASURES: Vital signs, pulmonary measures, physical work capacity, grip strength, and exercise duration were determined to be relevant outcomes. RESULTS: Three trials examined marijuana and its effects on athletic performance. Two trials had a high risk of bias and 1 trial had an unclear risk of bias. The effect of marijuana on outcomes including heart rate, blood pressure, and exercise duration remains unclear. Low quality evidence suggests that treatment, sham, and inactive control groups do not have a significant difference for grip strength. Low quality evidence suggests that there is an ergogenic effect of treatment demonstrated by increased bronchodilation and FEV1 compared with inactive control and that there is an ergolytic effect of treatment demonstrated by decreased physical work capacity compared with sham and inactive control groups. CONCLUSION: Because the number and quality of studies was low, the effects of marijuana on athletic performance remain unclear.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".