YouTube as a source of education in perioperative anesthesia for patients and trainees: a systematic review
Bibliographic record
Abstract
BACKGROUND: Online video sharing platforms like YouTube (Google LLC, San Bruno, CA, USA) have become a substantial source of health information. We sought to conduct a systematic review of studies assessing the overall quality of perioperative anesthesia videos on YouTube. METHODS: We searched Embase, MEDLINE, and Ovid for articles published from database inception to 1 May 2023. We included primary studies evaluating YouTube videos as a source of information regarding perioperative anesthesia. We excluded studies not published in English and studies assessing acute or chronic pain. Studies were screened and data were extracted in duplicate by two reviewers. We appraised the quality of studies according to the social media framework published in the literature. We used descriptive statistics to report the results using mean, standard deviation, range, and n/total N (%). RESULTS: Among 8,908 citations, we identified 14 studies that examined 796 videos with 59.7 hr of content and 47.5 million views. Among the 14 studies that evaluated the video content quality, 17 different quality assessment tools were used, only three of which were externally validated (Global Quality Score, modified DISCERN score, and JAMA score). Per global assessment rating of video quality, 11/13 (85%) studies concluded the overall video quality as poor. CONCLUSIONS: Overall, the educational content quality of YouTube videos evaluated in the literature accessible as an educational resource regarding perioperative anesthesia was poor. While these videos are in demand, their impact on patient and trainee education remains unclear. A standardized methodology for evaluating online videos is merited to improve future reporting. A peer-reviewed approach to online open-access videos is needed to support patient and trainee education in anesthesia. STUDY REGISTRATION: Open Science Framework ( https://osf.io/ajse9 ); first posted, 1 May 2023.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".