Medicinal cannabis for symptom control in advanced cancer: a double-blind, placebo-controlled, randomised clinical trial of 1:1 tetrahydrocannabinol and cannabidiol
Bibliographic record
Abstract
PURPOSE: Patients with cancer commonly access cannabis hoping to relieve their symptoms. This study assessed whether a 1:1 10 mg/ml THC:CBD combination oil could improve total symptom burden in patients with advanced cancer over that provided by palliative care alone. METHODS: Participants were randomised to medicinal cannabis (MC) or placebo oil; dose escalated over 14 days according to tolerance and efficacy and continued to day 28. Symptoms assessed using the Edmonton Symptom Assessment Scale (ESAS) were summated to give a total symptom distress score (TSDS). The primary outcome measure was the change from baseline in TSDS at day 14. Secondary outcomes included individual symptom scores, opioid use, participant-selected dose, QoL, psychological symptoms, global impression of change (GIC), and adverse effects. RESULTS: The pre-planned sample size of 120 at day 14 was reached following the randomisation of 144 patients. Mean (SD) TSDS improved over time in both arms (- 6.30 (12.3) MC, - 6.98 (12.56) placebo, p = 0.76) to day 14 with no difference between arms. A statistically significant improvement in ESAS pain scores in the MC arm (mean (SD) - 1.42 (2.15) MC and - 0.46 (2.83) placebo, p = 0.04) was at the expense of greater psychomimetic toxicity. Improvement in general well-being was greater for the placebo. GIC and the pain component of QoL both favoured MC. CONCLUSIONS: Patients can be informed that a 1:1 THC:CBD combination cannabis oil was no better than palliative care alone in palliating symptoms in patients with advanced cancer. A small benefit in pain control was associated with greater toxicity. TRIAL REGISTRATION: Australian New Zealand Clinical Trial Registry (ANZCTR): ACTRN12619000037101, 14/01/2019.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".