Effects of sub-chronic nabiximols on biological markers of individuals undergoing a clinical trial for the treatment of cannabis use disorder.
Bibliographic record
Abstract
OBJECTIVE: Nabiximols is used for treating various symptoms associated with multiple sclerosis (MS). Nabiximols is also being investigated as a potential treatment medication for individuals with cannabis use disorder (CUD). A variety of investigations have shown that, at low doses, nabiximols is overall well tolerated for MS treatment. However, due to tolerance, the management of CUD would likely require much higher doses of nabiximols to be effective. The effects of high doses of nabiximols on clinical laboratory tests remain unclear. Therefore, we investigated the sub-chronic effects of high doses of nabiximols on liver function, renal function, and other routine blood tests in this prospective study. METHODS: We performed a secondary analysis of various blood markers results collected during a double-blind, placebo-controlled randomized clinical trial (Sativex and Behavioral-relapse Prevention Strategy in Cannabis Dependence, NCT01747850, https://clinicaltrials.gov/ct2/show/record/NCT01747850). This trial tested the impact of the 12-week administration of nabiximols with a maximum daily dose of up to 113.4 mg THC/105 mg CBD. RESULTS: The measurements of the various biomarkers were in the normal range during the 12-week time frame. The results indicate an overall good tolerability of high-dose nabiximols on the blood markers measured. CONCLUSION: Our preliminary results suggest that high doses of nabiximols might be well tolerated by individuals with CUD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".