Medical Cannabis for Patients Over Age 50: A Multi-site, Prospective Study of Patterns of Use and Health Outcomes
Notice bibliographique
Résumé
Objective: Cannabis is being used as a therapeutic option by patients around the globe, and older patients represent a rapidly growing subset of this population. This study aims to assess the patterns of medical cannabis use in patients over 50 years of age and its effect on health outcomes such as pain, sleep, quality of life, and co-medication. Method: The Medical Cannabis in Older Patients Study (MCOPS) is a multi-site, prospective observational study examining the real-world impact of medical cannabis use on patients over age 50 under the guidance of a health care provider. The study included validated instruments, with treating physicians collecting detailed data on participant characteristics, medical cannabis and co-medication use, and associated impacts on pain, sleep, quality of life, as well as adverse events. Results: Inclusion criteria were met by 299 participants. Average age of participants was 66.7 years, and 66.2% of respondents identified as female. Approximately 90% of patients used medical cannabis to treat pain-related conditions such as chronic pain and arthritis. Almost all patients reported a preference for oral cannabis products (e.g., extracts, edibles) rather than inhalation products (e.g., flower, vapes), and most preferred oral formulations high in cannabidiol and low in tetrahydrocannabinol. Over the six-month study period, significant improvements were noted in pain, sleep, and quality of life measures, with 45% experiencing a clinically meaningful improvement in pain interference and in sleep quality scores. Additionally, nearly 50% of patients taking co-medications at baseline had reduced their use by the end of the study period, and quality of life improved significantly from baseline to M3 and from baseline to M6, with an incremental cost per quality-adjusted life-year (QALY) of $25,357.20. No serious adverse events (SAEs) were reported. Conclusions: In this cohort of older patients, most of whom suffered from pain-related conditions, medical cannabis seemed to be a safe and effective treatment. Most patients experienced clinically significant improvements in pain, sleep, and quality of life and reductions in co-medication. The cost per QALY was well below the standard for traditional pharmaceuticals, and no SAEs were reported, suggesting that cannabis is a relatively safe and cost-effective therapeutic option for adults dealing with age-related health conditions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».