Cannabis Use in Knee Osteoarthritis
Notice bibliographique
Résumé
Background Knee osteoarthritis (OA) is a prevalent and progressive joint disease, significantly impacting morbidity, disability, and healthcare utilization. Conventional treatments often fall short in managing knee OA effectively, prompting exploration into alternative therapies like cannabis. This project aimed to expand on the limited understanding of cannabis in managing knee OA. It aimed to characterize current management strategies and cannabis use patterns among individuals with knee OA, as well as to assess patient-perceived efficacy and tolerability of cannabis in knee OA. Methods An anonymous survey was distributed to patients through physician and nurse practitioner clinics in Saskatchewan. In addition to participant demographics, the questionnaire assessed disease severity and the treatments used to manage symptoms of OA. Specific questions were asked to participants who indicated they used cannabis to investigate formulations used, routes of administration, perceived effectiveness, and tolerability. Data from the survey was analyzed descriptively. Results Invitation packages were distributed to 205 people with knee OA from an orthopedic surgeon’s office and two primary healthcare centers. A total of 89 participants completed the survey achieving an overall response rate of 43.4%. The majority of participants were white (n=78, 87.6%), over 65 years old (n=58, 62.2%), and retired (n=61, 68.5%). Acetaminophen was the most commonly used pharmacologic agent with 60 participants (67.4%) reporting its use. It was followed by topical (n=46 participants, 51.7%) and oral non-steroidal anti-inflammatory drugs (NSAIDs) (n=39 participants, 43.8%). Nearly half of the respondents used exercise as a management strategy (n=42, 48.3%). Cannabis was currently being used to manage symptoms by 17.6% of the participants, with 62.5% reporting improvements in pain and 68.8% reporting improvements in sleep. Additionally, five cannabis users (31.3%) noted a reduction in the amount of other pain medications being used. There was significant variation in the dose, formulation, and route of administration of cannabis products used, and the majority of products were purchased from retail cannabis stores. Conclusion While conventional pharmacological treatments, particularly acetaminophen and NSAIDs, remain predominant in managing knee OA, a notable proportion of participants also reported using cannabis. Cannabis was perceived to be particularly effective for pain and sleep improvement, albeit with variable impacts on physical function, swelling, stiffness, and mental and social health and with a wide range of reported doses and formulations used. The lack of standardized information on safe and effective cannabis regimens for medical purposes presents a significant challenge for healthcare providers who must be equipped to facilitate conversations with patients, given the prevalence of cannabis use. Future research is crucial for determining the optimal formulation and dose of cannabis for managing knee OA and controlled clinical trials are necessary for establishing efficacy and safety.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».