Cannabinoid Analgesia as a Potential New Therapeutic Option in the Treatment of Chronic Pain
Bibliographic record
Abstract
OBJECTIVE: To review the literature concerning the physiology of the endocannabinoid system, current drug development of cannabinoid agonists, and current clinical research on the use of cannabinoid agonists for analgesia. DATA SOURCES: Articles were identified through a search of MEDLINE (1966-August 2005) using the key words cannabis, cannabinoid, cannabi*, cannabidiol, nabilone, THC, pain, and analgesia. No search limits were included. Additional references were located through review of the bibliographies of the articles identified. STUDY SELECTION AND DATA EXTRACTION: Studies of cannabinoid agonists for treatment of pain were selected and were not limited by pain type or etiology. Studies or reviews using animal models of pain were also included. Articles that related to the physiology and pharmacology of the endocannabinoid system were evaluated. DATA SYNTHESIS: The discovery of cannabinoid receptors and endogenous ligands for these receptors has led to increased drug development of cannabinoid agonists. New cannabimimetic agents have been associated with fewer systemic adverse effects than delta-9-tetrahydrocannabinol, including recent development of cannabis medicinal extracts for sublingual use (approved in Canada), and have had promising results for analgesia in initial human trials. Several synthetic cannabinoids have also been studied in humans, including 2 cannabinoid agonists available on the international market. CONCLUSIONS: Cannabinoids provide a potential approach to pain management with a novel therapeutic target and mechanism. Chronic pain often requires a polypharmaceutical approach to management, and cannabinoids are a potential addition to the arsenal of treatment options.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".