E066 Using social network platforms to evaluate patient perceptions of CBD products in the UK, US, and Canada
Notice bibliographique
Résumé
Abstract Background/Aims The number of patients using CBD products have become increasingly popular for the treatment of several conditions including rheumatic and musculoskeletal diseases. Cannabidiol (CBD) products often claim numerous benefits to health, however there is limited scientific data relating to why patients take these products, their safety and efficacy since these products are not regulated. Social media provides a valuable platform for patients to discuss their positive/negative medication experiences and seek peer support. The aim of this study was to harnesses the benefits of using social media for research by evaluating patient perceptions of CBD products within the UK, US and Canada. Methods HealthUnlocked is one of the most popular social platforms for health across Europe with over 1.5 million users in 500 communities, covering over 250 health conditions. Over 250,000 HealthUnlocked posts between the 1st of March 2020 to 17th of September 2021 were extracted and anonymized. Posts only from users who consented to share their anonymised data for research were included. CBD specific terms were then used to filter related posts from the UK, US, and Canada for manual thematic analysis following removal of duplicates. Analysis was performed focusing on the following themes: indications of CBD products, potential benefits and limitations including patient reported side-effects associated with CBD, and concomitant treatments with CBD products. Results were then compared between country where possible. Results For this study, 339 CBD-related posts from HealthUnlocked met eligibility criteria. The most common indications for CBD products in the UK were fibromyalgia/chronic pain (29.1%), restless leg syndrome (8.5%) and rheumatoid arthritis (7.5%). In the US, the most common were anxiety/stress (23.0%), cancer (15.9%) and chronic pain (11.1%). Of all posts that discussed benefits of CBD (31.5%), these included helping with pain (47.6%), anxiety (41.1%), and insomnia relief (22.4%). Most common limitations included CBD inefficacy (41.3%) and potential drug-drug interactions (24%) including warfarin, levothyroxine and prednisolone. Patient-reported side effects included drowsiness, diarrhoea, tachycardia and possible hallucinations. Opioid analgesics were the top mentioned concomitant treatment with CBD products (14.3%), followed by antidepressants (12.8%), NSAIDs (11.2%) and corticosteroids (11.2%) across all countries. Conclusion There were differences in the indications of CBD usage between countries. UK patients reported CBD being used more in fibromyalgia/chronic pain and rheumatoid arthritis, whereas US patients used them for anxiety/depression and cancer. CBD inefficacy, drug-drug interactions and side-effects were frequently discussed. Narrative patient experiences from social media can provide new insights about unregulated products such as CBD and provide opportunities to generate new research questions that are important to patients. Disclosure S. Minhas: None. M. Jani: None.
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 | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».