Consensus recommendations on dosing and administration of medical cannabis to treat chronic pain: results of a modified Delphi process
Bibliographic record
Abstract
BACKGROUND: Globally, medical cannabis legalization has increased in recent years and medical cannabis is commonly used to treat chronic pain. However, there are few randomized control trials studying medical cannabis indicating expert guidance on how to dose and administer medical cannabis safely and effectively is needed. METHODS: Using a multistage modified Delphi process, twenty global experts across nine countries developed consensus-based recommendations on how to dose and administer medical cannabis in patients with chronic pain. RESULTS: There was consensus that medical cannabis may be considered for patients experiencing neuropathic, inflammatory, nociplastic, and mixed pain. Three treatment protocols were developed. A routine protocol where the clinician initiates the patient on a CBD-predominant variety at a dose of 5 mg CBD twice daily and titrates the CBD-predominant dose by 10 mg every 2 to 3 days until the patient reaches their goals, or up to 40 mg/day. At a CBD-predominant dose of 40 mg/day, clinicians may consider adding THC at 2.5 mg and titrate by 2.5 mg every 2 to 7 days until a maximum daily dose of 40 mg/day of THC. A conservative protocol where the clinician initiates the patient on a CBD-predominant variety at a dose of 5 mg once daily and titrates the CBD-predominant dose by 10 mg every 2 to 3 days until the patient reaches their goals, or up to 40 mg/day. At a CBD-predominant dose of 40 mg/day, clinicians may consider adding THC at 1 mg/day and titrate by 1 mg every 7 days until a maximum daily dose of 40 mg/day of THC. A rapid protocol where the clinician initiates the patient on a balanced THC:CBD variety at 2.5-5 mg of each cannabinoid once or twice daily and titrates by 2.5-5 mg of each cannabinoid every 2 to 3 days until the patient reaches his/her goals or to a maximum THC dose of 40 mg/day. CONCLUSIONS: In summary, using a modified Delphi process, expert consensus-based recommendations were developed on how to dose and administer medical cannabis for the treatment of patients with chronic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".