P013 UNREGULATED: MEDICAL COMPANIES USE SOCIAL MEDIA TO SELL ALTERNATIVE TREATMENTS FOR INFLAMMATORY BOWEL DISEASE
Bibliographic record
Abstract
Patients are increasingly using social media platforms to inform themselves about their medical diagnoses and explore potential treatment options. The medical industry has begun to utilize social media to communicate directly with potential customers. It is crucial that healthcare professionals are aware of this direct communication and realize that such interactions may significantly influence patients’ decisions about disease management. This study evaluated Twitter messaging by the medical sales industry promoting non-conventional treatments for inflammatory bowel disease (IBD) and assessed origin and content of relevant tweets. The Social Feed Manager software (SFM; version 1.10.0: GW University, 2017) accessed Twitter’s programming interface to obtain content and geographic origin of tweets related to IBD during a 10-day period. SFM queried Twitter using terms including “Crohn’s”, “Ulcerative Colitis”, “IBD”, “and “Inflammatory Bowel Disease” and located origin through the geotag function. Retweets, non-English messages, and tweets that were removed were excluded from analysis. Individual tweets were read and analyzed for content. Statistical analysis was performed using two-tailed Fisher’s Exact test with significance set at p<0.05. 178 tweets originated from the medical sales industry with 97 analyzed after exclusion criteria. 77 (79.4%) messages were geotagged with country of origin, revealing that tweets primarily originated from the US (38; 49.4%), United Kingdom (25; 32.4%) and Canada (9; 11.6%). 19 (24.7%) tweets contained information related to alternative treatments for IBD. Of these 19, 14 were from an identifiable a location: 10 (71.4%) US, 2 (14.2%) UK, 1 (7.1%) from Canada, and 1 (7.1%) from India. These 14 geographically identifiable tweets advertised cannabis/cannabinoid oils (5, 35.7%), antifungals/probiotics (3, 17.6%), and aloe, amino acids, hot peppers, Zeolite, yeast, and plant DNA fragments (6, 42.9%; each with one). Significantly more messages promoting non-conventional treatment options for IBD originated from the US when compared to the UK (p=0.03), Canada (p=0.007) and India (p=0.007). This study revealed that Twitter is a global tool being used to share information regarding alternative therapies for IBD. While this study was limited by Twitter’s determination of relevance and allowable time frame collection, it is important to recognize that social media platforms such as Twitter offer easily accessible, unregulated information directly to patients. As many patients with IBD utilize or consider alternative treatments, it is essential for clinicians to be aware that patients’ therapeutic decisions can be influenced by the medical sales industry’s use of social media. It is critical that healthcare providers are aware of the medical sales industry’s utilization of social media platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.023 |
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".