P-15 Clinical studies of medicinal cannabis in palliative care – The development of a research program
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,181 | 0,148 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».