Inflammatory Bowel Disease and X (Formerly Twitter) Influencers: Who Are They and What Do They Say?
Bibliographic record
Abstract
Background and objective More than half of the population suffering from inflammatory bowel disease (IBD) use the internet as a primary source of information on their condition. X (formerly Twitter) has been increasingly used to disseminate healthcare-related information. In this study, we aimed to identify top influencers on the topic of IBD on X and correlate the relevance of their social media engagements with their professional expertise or academic productivity. Methods X (formerly Twitter) influence scores for the search topic IBD were obtained using Cronycle API, a proprietary software employing multiple algorithms to rank influencers. Data regarding gender, profession, location, and research productivity represented as h-index was collected. Results We collected information on the top 100 IBD influencers on X. The majority of influencers were gastroenterologists, followed by IBD advocates. Of note, 62% of the IBD influencers were from the US followed by the UK and Canada. A positive correlation was observed between the X topic score and the h-index of the influencer (r=+0.488, p<0.001) Conclusions The strong correlation observed between the X topic score and h-index suggests that social media is a viable platform for gaining information regarding IBD. Further research aimed at counteracting misleading information by providing facts and data in a succinct manner about IBD on social media is required to improve disease awareness.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".