Neuroprotection Following Concussion: The Potential Role for Cannabidiol
Bibliographic record
Abstract
Cannabidiol (CBD) has been generating increasing interest in medicine due to its therapeutic properties and an apparent lack of negative side effects. Research has suggested that high dosages of CBD can be taken acutely and chronically with little to no risk. This review focuses on the neuroprotective effects of a CBD, with an emphasis on its implications for recovering from a mild traumatic brain injury (TBI) or concussion. CBD has been shown to influence the endocannabinoid system, both by affecting cannabinoid receptors and other receptors involved in the endocannabinoid system such as vanilloid receptor 1, adenosine receptors, and 5-hydroxytryptamine via cannabinoid receptor-independent mechanisms. Concussions can result in many physiological consequences, potentially resulting in post-concussion syndrome. While impairments in cerebrovascular and cardiovascular physiology following concussion have been shown, there is unfortunately still no single treatment available to enhance recovery. CBD has been shown to influence the blood brain barrier, brain-derived neurotrophic factors, cognitive capacity, the cerebrovasculature, cardiovascular physiology, and neurogenesis, all of which have been shown to be altered by concussion. CBD can therefore potentially provide treatment to enhance neuroprotection by reducing inflammation, regulating cerebral blood flow, enhancing neurogenesis, and protecting the brain against reactive oxygen species. Double-blind randomized controlled trials are still required to validate the use of CBD as medication following mild TBIs, such as concussion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".