Non-Intoxicating Cannabinoids in Visceral Pain
Bibliographic record
Abstract
Cannabis and cannabis products are becoming increasingly popular options for symptom management of inflammatory bowel diseases, particularly abdominal pain. While anecdotal and patient reports suggest efficacy of these compounds for these conditions, clinical research has shown mixed results. To date, clinical research has focused primarily on delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD). THC is a ligand of classical cannabinoid receptors (CBRs). CBD is one of a large group of nonintoxicating cannabinoids (niCBs) that mediate their effects on both CBRs and through non-CBR mechanisms of action. Because they are not psychotropic, there is increasing interest and availability of niCBs. The numerous niCBs show potential to rectify abnormal intestinal motility as well as have anti-inflammatory and analgesic effects. The effects of niCBs are frequently not mediated by CBRs, but rather through actions on other targets, including transient receptor potential channels and voltage-gated ion channels. Additionally, evidence suggests that niCBs can be combined to increase their potency through what is termed the entourage effect. This review examines the pre-clinical data available surrounding these niCBs in treatment of abdominal pain with a focus on non-CBR mechanisms.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".