YouTube videos for describing Deep Brain Stimulation: a comprehensive and quantitative review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction Patients use online videos to learn about their condition and potential treatments. Operative techniques in Deep Brain Stimulation (DBS) vary significantly between institutions. This poses challenges to ensuring patients are adequately and accurately informed. We performed a comprehensive review of YouTube videos describing Deep Brain Stimulation.Methods Text searches for DBS-related search strings were performed on YouTube. The top 25 de-duplicated videos per search were included. Each video was assessed for differences in procedural technique, educational quality using the JAMA benchmark and DISCERN tools, and audio-visual or editing quality.Results We identified 91 DBS-related YouTube videos with 44% of videos uploaded by academic institutions and 15% by hospitals. Parkinson’s disease was the most frequently described condition in 65% of videos. Variations in procedure impacting patient experience and expectations, were discussed in varying proportions: head shaving in 14.3% of videos, potential complications in 23.1%, number of stages in 33.0%, and awake vs asleep surgery in 46.2%. The JAMA benchmark criteria was fulfilled in 12% of videos and the median total DISCERN score was 46, an ‘average’ quality rating. High-quality images (N = 69, 75.8%), audio/music (N = 73, 80.2%), accessible language (N = 84, 92.3%), and professional production quality (N = 72, 79.1%) were present in most videos.Discussion and conclusion YouTube videos describing DBS are visually appealing but lack scientific quality and present potentially misleading content for future DBS recipients and caregivers. They should be viewed with caution as a source of medical communication or information for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it