The origin of YouTube videos on hereditary angioedema matters
Bibliographic record
Abstract
BACKGROUND: Hereditary angioedema (HAE) is a rare, potentially life-threatening condition that requires accessible and reliable information. YouTube has emerged as a significant source of health-related content, offering valuable insights while posing the risk of misinformation that warrants caution among users. The aim of this study was to evaluate the popularity, reliability, understandability, actionability, and overall quality of YouTube videos related to HAE. METHOD: A search was conducted on YouTube using the term "hereditary angioedema." Videos were categorized based on their origin (health or nonhealth) and content type (medical professional education (MPE), patient education (PE), patient experience, or awareness). The quality, reliability, understandability, and actionability of the videos were assessed via the Global Quality Scale (GQS), the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V), and the Quality Criteria for Consumer Health Information (DISCERN) tool. Three independent allergists evaluated the videos. RESULTS: Out of 135 reviewed videos, 111 met the inclusion criteria. The health group presented significantly higher scores than did the nonhealth group in several metrics: PEMAT-A/V understandability (83, IQR: 56-92, p = 0.001), total DISCERN score (37, IQR: 3-45, p < 0.001), reliability (23, IQR: 19-26, p < 0.001), treatment (15, IQR: 8-21, p = 0.007), and modified DISCERN score (3, IQR: 2-4, p = 0.002). Health videos were uploaded more recently (p = 0.006), while awareness videos tended to be older than more recent MPE videos (p = 0.002). The MPE videos had the longest duration, whereas the awareness videos had the shortest duration (p < 0.001). Video quality scores, assessed via the GQS, were higher in both the MPE and PE groups (scores: 3, 4, and 5; p = 0.005). Compared with the other groups, the MPE group also had significantly higher PEMAT-A/V understandability scores (91, IQR: 70.75-92, p < 0.001), total DISCERN scores (40, IQR: 30.75-49.5, p < 0.001), reliability scores (24, IQR: 21-27.25, p < 0.001), and overall scores for moderate to high quality (83, 74.8%, p = 0.002). CONCLUSION: YouTube videos on HAE uploaded by health care professionals generally offer higher-quality information, but their overall reliability remains suboptimal. There is a pressing need for higher-quality, trustworthy content, particularly from professional medical organizations, to address this gap.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".