<p>Cannabis Extract CT-921 Has a High Efficacy–Adverse Effect Profile in a Neuropathic Pain Model</p>
Bibliographic record
Abstract
BACKGROUND: Legalization of cannabis encourages the development of specific cultivars to treat disease such as neuropathic pain. Because of the large number of cultivars, it is necessary to prioritize extracts before proceeding to clinical trials. PURPOSE: To compare extracts of two unique cannabis cultivars (CT-921, CT-928) for treatment of neuropathic pain induced by constriction of sciatic nerve in mice and to illustrate the use of this animal model to set priority for future trials. METHODS: Pain severity was measured by threshold force causing paw withdrawal. Dose-response relationships and time course were determined for intravenously injected extracts of cultivars and vehicle. The doses for allodynia relief were correlated with decreased respiratory rate, temperature and behavioral changes. RESULTS: , administration of CT-928 significantly decreased respiratory rate while CT-921 did not. CT-928 decreased temperature more than CT-921. CT-928 produced frantic hyperactivity not seen with CT-921. At equivalent analgesic doses, THC was much less in CT-921 than in CT-928 suggesting interactions with components other than THC influenced the analgesia. At equivalent analgesic doses, efficacy-to-adverse effect profile for CT-928 was worse than for CT-921. CONCLUSION: Both extracts relieved neuropathic pain; however, CT-921 had a better efficacy-to-adverse effect profile, a rational basis for prioritizing cultivars for future clinical evaluation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".