The Use of Medical Cannabis with Other Medications: A Review of Safety and Guidelines - An Update [Internet]
Notice bibliographique
Résumé
According to Statistics Canada’s National Cannabis Survey, approximately 2.7 million Canadians (9%) reported using cannabis for medical reasons in the first half of 2019.For the purposes of this report, medical cannabis refers to use of the cannabis plant or its extracts or synthetic cannabinoids for medical purposes.The list of medical conditions for which cannabis may be of benefit is extensive and includes chemotherapy-induced nausea and vomiting, cachexia, anorexia nervosa, multiple sclerosis, amyotrophic lateral sclerosis, spinal cord injury and disease, epilepsy, pain, and many others.The cannabis plant contains hundreds of pharmacological components, not all of which are well-characterized. Presence and quantity of these components varies considerably between plants and products, and even within a single plant. Tetrahydrocannabinol (THC) is the most well-studied and is the primary pharmacological component responsible for the psychoactive and physical effects of cannabis. Cannabidiol (also commonly referred to as CBD) is the second most prevalent pharmacologically active compound. It does not have psychotropic properties, but is also proposed to be of value for treatment of a wide variety of medical conditions., Producers of cannabis and cannabis extracts for medical purposes are able to supply products with specific desired quantities and ratios of THC and cannabidiol. Two medical cannabis products are currently marketed for use in Canada. Nabiximols (Sativex) is a prescription product containing THC and cannabidiol. Nabilone (Cesamet) is a prescription synthetic cannabinoid product available in Canada. Synthetic cannabinoids mimic the effects of the active components of cannabis.Cytochrome P450 (CYP450) enzymes play a crucial role in the metabolism of many medications and are the main drivers of pharmacokinetic drug interactions. THC and cannabidiol are known substrates and modulators of the CYP450 enzyme system. THC and cannabidiol have shown to inhibit several CYP450 enzymes in vitro, whereas smoke from cannabis may induce one specific CYP450 enzyme. Along with a lack of robust clinical studies, inconsistency in chemical make-up and method of ingestion of various cannabis products makes it particularly difficult to clinically assess for and predict interactions between cannabis and other medications. In addition to the potential pharmacokinetic interactions due to changes in drug metabolism, the risk of pharmacodynamic interactions (e.g., adverse effects secondary to the use of cannabis with other psychoactive drugs) is also important to consider.,The aim of this report is to review the evidence surrounding safety of medical cannabis in combination with other medications, and relevant evidence-based guidelines.This is an update of a previous report published in April 2017, which found a single systematic review, and no evidence-based guidelines.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».