YouTube as a Source of Patient and Trainee Education in Vascular Surgery: A Systematic Review
Bibliographic record
Abstract
Due to its video based approach, YouTube has become a widely accessed educational resource for patients and trainees. This systematic review characterised and evaluated the peer reviewed literature investigating YouTube as a source of patient or trainee education in vascular surgery. A comprehensive literature search was conducted using EMBASE, MEDLINE, and Ovid HealthStar from inception until 19 January 2023. All primary studies and conference abstracts evaluating YouTube as a source of vascular surgery education were included. Video educational quality was analysed across several factors, including pathology, video audience, and length. Overall, 24 studies were identified examining 3 221 videos with 123.1 hours of content and 37.1 million views. Studies primarily examined YouTube videos on diabetic foot care (7/24, 29%), peripheral arterial disease (3/24, 13%), carotid artery stenosis (3/24, 13%), varicose veins (3/24, 13%), and abdominal aortic aneurysms (2/24, 8%). Video educational quality was analysed using standardised assessment tools, author generated scoring systems, or global author reported assessment of quality. Six studies assessed videos for trainee education, while 18 studies evaluated videos for patient education. Among the 20 studies which reported on the overall quality of educational content, 10/20 studies deemed it “poor”, and 10/20 studies considered it “fair”, with 53% of studies noting poor educational quality for videos intended for patients and 40% of studies noting poor educational quality in videos intended for trainees. Poor quality videos had more views than fair quality videos (mean [95% CI] 27 348, 15 154 – 39 543 views vs. 11 372, 3 115 – 19 629 views, p = .030). The overall educational quality of YouTube videos for vascular surgery patient and trainee education is suboptimal. There is significant heterogeneity in the quality assessment tools used in their evaluation. A standardised approach to online education with a consistent quality assessment tool is required to better support online patient and trainee education in vascular surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".