P-15 Clinical studies of medicinal cannabis in palliative care – The development of a research program
Bibliographic record
Abstract
Background Medicinal Cannabis (MC) was legalised in Australia in 2016 for a range of indications including palliative care, despite a lack of research evidence. Patients with advanced cancer in the community commonly access cannabis aiming to improve their symptoms. Following the award of grants from the NHMRC – Medical Research Future Fund in 2018 and 2020, we have developed a medicinal cannabis research program. Objectives To define the role (if any) of cannabinoids in patients with symptoms from advanced cancer. To conduct robust phase three clinical trials to contribute to the evidence base for medicinal cannabis prescribing in Australia. Methods Develop and complete a pilot study to test the feasibility of a larger RCT with an MC product in an advanced cancer patient population and develop phase 3 MC trials 1, 2 and 3 of different products and concentrations. Explore qualitative studies around patient use and views of MC products in our community and conduct sub studies testing the anti-inflammatory properties of cannabinoids. Investigate the detection of tetrahydrocannabionol (THC) medicinal products in relation to motor vehicle driving and real-world implications. Conduct post trial long term surveillance of marketed products using the authorised prescriber scheme. Results In the pilot study 86% of recruits completed the primary outcome with 46% meeting the definition of response. The study drug was well tolerated. MedCan 1, a cannabidiol (CBD) versus placebo RCT (n=144), showed all components of the Edmonton Symptom Assessment Scale (ESAS) improved (fell) over time with no difference between arms. There was no detectable effect of CBD on quality of life, depression, or anxiety. Adverse events did not differ significantly between arms apart from dyspnoea that was more common with CBD. Most participants reported feeling better or much better at days 14 and 28. In MedCan 2, a (THC)/CBD 1:1 versus placebo RCT, the results showed no total ESAS difference between arms. There was a significant difference in reduction in ESAS pain scores at day 14 (mean (SD) -1.41 (2.15) MC, -0.46 (2.82) placebo), p = 0.04 in favour of MC. Adverse events of special interest revealed an increased incidence of confusion, feeling high, and exaggerated sense of well-being in the MC arm. In a C-reactive protein sub study, we were unable to demonstrate an anti-inflammatory effect of CBD in cancer patients. Discussion Medicinal cannabis is commonly used in the community by people with cancer to treat the associated troubling symptoms of their disease and treatment. Our trials have been designed to define the best and safest place for MC in supportive care. Our results have been included in systematic reviews, meta-analyses, and international guidelines. Health consumers have provided valuable input into the design of our trials and ongoing safety monitoring. Currently we have 15 publications with MedCan 3, MedCan Drive, MedCan Inflam, MedCan Post trial, and two qualitative studies still in the recruitment phase. Our results will continue to inform policy and practice around MC prescribing both nationally and internationally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.181 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".