An Update of Current Cannabis-Based Pharmaceuticals in Pain Medicine
Bibliographic record
Abstract
Cannabis users have long reported therapeutic properties of the plant for a variety of conditions, some of which include nausea, emesis, seizures, cancer, neurogenic diseases and pain control. Research has elucidated many cannabinoid pharmacodynamic and pharmacokinetic properties, expanding the potential use of cannabinoids as a medical therapy. Due to the inconsistent delivery and control of the active components involved with smoking, pharmaceutical companies are investigating and prioritizing routes other than smoke inhalation for therapeutic use of cannabinoids. In this relatively new field of pharmaceutical development, ongoing drug development promises great benefit from targeted endocannabinoid receptor agonism. Available in Canada and Europe, nabiximols, a specific extract from the Cannabis plant, has demonstrated great benefit in the treatment of pain related to spasticity in multiple sclerosis, cancer and otherwise chronic pain conditions. The cannabidiol oral solution Epidiolex®, which is available in the USA, is indicated for management of refractory epilepsy but may offer therapeutic relief to chronic pain conditions as well. Current investigative drugs, such as those developed by Cara Therapeutics and Zynerba Pharmaceuticals, are synthetic cannabinoids which show promise to specifically target neuropsychiatric conditions and chronic pain symptoms such as neuropathy and allodynia. The objective of this review is to provide clinicians with an update of currently available and promising developmental cannabis pharmaceutical derivatives which may stand to greatly benefit patients with otherwise difficult-to-treat chronic conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".