A type II cannabis extract and a 1:1 blend of Δ(9)-tetrahydrocannabinol and cannabidiol display distinct antinociceptive profiles and engage different endocannabinoid targets when administered into the subarachnoid space
Bibliographic record
Abstract
Introduction: Cannabis extracts are being increasingly used to mitigate chronic pain. Current guidelines for their prescription rely on Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) content as well as the ratio of these major cannabinoids present in the blend. Here we assessed whether these descriptors were representative of product effectiveness to produce a desired outcome such as analgesia. Methods: In this study, we used a rat model of diabetic neuropathy and assessed the reduction in mechanical allodynia following intrathecal injection of pure THC, pure CBD, a 1:1 mix of these compounds and a “balanced” chemotype II cannabis extract. Engagement of endocannabinoid targets by different treatments was investigated using CB1 (AM251) and CB2 (AM630) receptor antagonists as well as a TRPV1 channel blocker (capsazepine). Results: Antinociceptive responses induced by an equivalent amount of THC administered in its pure form, as a THC:CBD mix or as a “balanced” extract were distinct. Furthermore, the 1:1 THC:CBD mix and the balanced extract had not only different response profiles but their relative engagement of CB1, CB2 receptors and TRPV1 channels was distinct. Discussion: These findings indicate that antinociceptive responses and targets engaged by blended cannabinoids are composition-specific, and cannot be simply inferred from THC and CBD contents. This information may have implications in relation to the way medicinal cannabis products are prescribed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".