Phase IIb Randomized, Placebo-Controlled, Dose-Escalating, Double-Blind Study of Cannabidiol Oil for the Relief of Symptoms in Advanced Cancer (MedCan1-CBD)
Bibliographic record
Abstract
PURPOSE To determine whether cannabidiol (CBD) oil can improve symptom distress in patients with advanced cancer receiving palliative care. METHODS Participants were adults with advanced cancer and symptom distress (Edmonton Symptom Assessment Scale [ESAS] total score of ≥ 10/90) who received titrated CBD oil 100 mg/mL, 0.5 mL once daily to 2 mL three times a day, or matched placebo for 28 days. The primary outcome was ESAS total symptom distress score (TSDS) at day 14. Response was defined as a decrease in TSDS by ≥ 6 at day 14. Secondary outcomes were ESAS TSDS over time, individual symptom scores, patient-determined effective dose, opioid use, Global Impression of Change, depression, anxiety, quality of life, and adverse events. RESULTS Of the 144 patients randomly assigned, the planned sample size of 58 participants on CBD and 63 on placebo reached the primary analysis point (day 14). The unadjusted change in TSDS from baseline to day 14 was –6.2 (standard deviation, 14.5) for placebo and –3.0 (standard deviation, 15.2) for CBD with no significant difference between arms ( P = .24). Similarly, there was no detected difference in proportion of responders (placebo: 37 of 63 [58.7%], CBD: 26 of 58 [44.8%], P = .13). All components of ESAS improved (fell) over time with no difference between arms. The median dose of participant-selected CBD was 400 mg per day with no correlation with opioid dose. There was no detectable effect of CBD on quality of life, depression, or anxiety. Adverse events did not differ significantly between arms apart from dyspnea that was more common with CBD. Most participants reported feeling better or much better at days 14 (53% CBD and 65% placebo) and 28 (70% CBD and 64% placebo). CONCLUSION CBD oil did not add value to the reduction in symptom distress provided by specialist palliative care alone.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".