Evaluation of Educational YouTube Videos for Distal Radius Fracture Treatment
Bibliographic record
Abstract
Purpose: Distal radius fractures (DRFs) are one of the most common fractures in adults. Adequate patient education is crucial for adherence to treatment. YouTube is a popular, accessible resource that has become a valuable tool for obtaining health information. The current study evaluated the top 50 YouTube videos on DRF treatment for patient education. Methods: A systematic search was conducted on YouTube using three searches to obtain 150 videos. Duplicate, nonrelevant, paid, and non-English videos were removed, and the top 50 rank-ordered videos were reviewed and characterized in terms of general (views, likes, video length, and publication date), source (publisher affiliation, presenter type, and target audience), and content (media type, topic coverage, advertisements, and bias) parameters. Results: Only 56% of videos were directed toward patients versus 40% for health care providers, highlighting a gap in patient-oriented educational content on YouTube. Most (86%) videos included effective visual aids, aligning with best practices for educational videos. Surgical management was overrepresented in 64% of the videos as opposed to nonsurgical management in 34% of videos. Only 31% of patient-oriented videos discussed surgical complications. Home exercises were emphasized in 75% of the videos discussing recovery topics. Conclusions: Although YouTube has the potential to be an effective resource for disseminating health information to patients, it has several limitations for education in DRF treatment including the lack of patient-oriented educational videos, overrepresentation of surgical treatment, and lack of information on surgical complications. Nonetheless, YouTube may have an important role as a supplementary resource, especially in certain topics such as guiding postoperative recovery with home exercises. Clinical relevance: This study allows health care providers and content creators to proactively address information gaps identified in educational YouTube videos on DRF treatment. It helps characterize the role of YouTube in supporting the treatment and recovery of patients experiencing DRFs.
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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 | 0.010 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".