Considering the Effects of Cannabinoids and Exercise on the Brain: A Narrative Review
Bibliographic record
Abstract
Recently, there has been a rising interest in the use of cannabis and its derivates as therapeutic tools to support brain health. Cannabis-based substances interact with the endogenous cannabinoid (i.e., endocannabinoid) system, comprised of an intricate network of cellular receptors, signaling proteins, and essential enzymes. The endocannabinoid system is involved in widespread physiological functions, including inflammation, vascular response, and neuronal transmission that influences brain function, positioning it as a prime target for brain health interventions. In other work, the benefits of exercise for brain health have been prominently noted. Such benefits are similarly attributed to influences on the immune, vascular, and nervous systems that promote overall brain health. Despite large bodies of work on both cannabinoid and exercise influences on brain function, there appears to be an understudied overlap in their physiological effects. Indirect and direct interactions between these two therapeutic avenues have potential to introduce additive, synergistic, or opposing effects that may need to be considered in applied work. In this narrative review, we describe the mechanisms and actors involved in the aforementioned physiological systems, with consideration of common and contrasting influences of cannabinoids and exercise on the brain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".