Pharmacological and physiological effects of cannabidiol: a dose escalation, placebo washout study protocol
Bibliographic record
Abstract
BACKGROUND: Cannabinoids such as cannabidiol (CBD) exhibit anti-inflammatory properties and have the potential to act as a therapeutic following mild traumatic brain injury. There is limited evidence available on the pharmacological, physiological and psychological effects of escalating CBD dosages in a healthy, male, university athlete population. Furthermore, no dosing regimen for CBD is available with implications of improving physiological function. This study will develop an optimal CBD dose based on the pharmacokinetic data in contact-sport athletes. The physiological and psychological data will be correlated to the pharmacokinetic data to understand the mechanism(s) associated with an escalating CBD dose. METHODS/DESIGN: Forty participants will receive escalating doses of CBD ranging from 5 mg CBD/kg/day to 30 mg CBD/kg/day. The CBD dose is escalated every two weeks in increments of 5 mg CBD/kg/day. Participants will provide blood for pharmacological assessments at each of the 10 visits. Participants will complete a physiological assessment at each of the visits, including assessments of cerebral hemodynamics, blood pressure, electrocardiogram, seismocardiogram, transcranial magnetic stimulation, and salivary analysis for genomic sequencing. Finally, participants will complete a psychological assessment consisting of sleep, anxiety, and pain-related questionnaires. DISCUSSION: This study will develop of an optimal CBD dose based on pharmacological, physiological, and psychological properties for future use during contact sport seasons to understand if CBD can help to reduce the frequency of mild traumatic injuries and enhance recovery. TRIAL REGISTRATION: Clinicaltrials.gov: NCT06204003.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".