YouTube for Information on Rheumatoid Arthritis — A Wakeup Call?
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is a common debilitating autoimmune disease, with unmet need for knowledge among patients and the general population. YouTube is a popular, consumer-generated, video-sharing website, which can be a source of information on RA. We investigated the quality of information on RA on YouTube and analyzed audience interaction. METHODS: YouTube was searched using the term "Rheumatoid Arthritis," for videos uploaded on RA. Two physicians independently classified videos as useful, misleading, or patient views, and rated them on a 5-point global quality scale (GQS; 1 = poor quality, 5 = excellent quality). Useful videos were rated for reliability and content, on a 5-point scale (higher scores represent more reliable and comprehensive videos). Source of videos was also noted. Audience interaction was assessed through video viewership. RESULTS: A total of 102 relevant videos were identified; 54.9% were classified as useful (GQS 2.9 ± 1.0) and 30.4% deemed misleading (GQS 1.3 ± 1.6). Mean reliability and content score of useful videos was 3.2 (± 1.0) and 2.5 (± 1.2), respectively. All videos uploaded by university channels and professional organizations provided useful information but formed only 12.7% of total videos, whereas 73.9% of medical advertisements and videos by for-profit organizations were misleading. There was no difference in the viewership/day (10.0 vs 21.5; p = nonsignificant) of useful and misleading information. CONCLUSION: YouTube is a source of information on RA, of variable quality, with wide viewership and potential to influence patients' knowledge and behavior. Physicians and professional organizations should be aware of and embrace this evolving technology to raise awareness about RA, and empower patients to discriminate useful from misleading information.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.031 |
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".