E066 Using social network platforms to evaluate patient perceptions of CBD products in the UK, US, and Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".