Cannabis for pain management: Pariah or panacea?
Bibliographic record
Abstract
Cannabis has been used in a medicinal context throughout recorded history and across diverse cultures to aid in the treatment of a wide array of ailments. Remarkably, clinical and preclinical investigations are only recently beginning to reveal the neurobiological mechanisms responsible for the clinically-relevant actions of cannabis that have been acknowledged by medical pharmacopeia for millennia. The therapeutic potential of cannabis-derived phytochemicals such as delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) are currently being explored in several contexts. Experimental evidence suggests that modulation of signal transduction pathways underlying cellular excitability, as well as interactions with the endocannabinoid and serotonin systems, which modulate emotion and pain sensitivity under physiological conditions, are among the mechanisms responsible for its clinical efficacy. Interestingly, the diverse pharmacodynamic profile of CBD suggests a synergistic interaction with current first- and second-line medications used in the treatment of neuropathic pain to produce clinically meaningful therapeutic benefits. To advance understanding of the neurobiological mechanisms underlying therapeutic cannabis use in pain management and to integrate its use into modern clinical practices, it is important to understand medicinal cannabis use in historic and medical contexts. This review highlights the copious history of medical practices incorporating the use of cannabis, and discusses the potential pharmacological mechanisms responsible for its therapeutic efficacy in the management of neuropathic 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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".