Assessment of educational YouTube videos on proximal humeral fracture treatment (YouTube videos on proximal humeral fractures)
Bibliographic record
Abstract
Background: Social media platforms have become principal sources of information for patients to gather information before clinic visits. YouTube is a popular platform for educational videos, and since proximal humeral fractures (PHFs) are a common orthopedic trauma injury, this study aimed to assess the characteristics of YouTube videos on PHF and PHF treatment. Methods: The terms "Proximal Humeral Fracture" and "Proximal Humeral Fracture Treatment" were used to gather the videos included in this study. Terms were searched programmatically using YouTube's search Application Program Interface. Top 50 videos from each search term were recorded and combined for a total of 100 videos. Duplicate videos were removed, and the remaining videos were rank ordered by the frequency and order of appearance from the initial search. Any non-English videos or videos irrelevant to the topic were excluded. The first 50 rank-order videos were included. Data collected were categorized into general parameters (eg, number of views, video length), source parameters (eg publisher affiliation, number of subscribers), and video content (eg, topic discussed, media type). Data were analyzed by 4 themes of basic information, information for health-care professionals, treatment, and rehabilitation. Each theme was further categorized by relevant subthemes. Results: Publisher affiliation of the PHF videos was most commonly commercial (56%). Health-care professionals or students were the more common target audience (62%) than patients (36%). The predominant media type used in the videos was lecture-style presentation (52%), followed by demonstration (32%), and interviews (18%). Sixty-two percent of the videos discussed basic information on PHF, such as epidemiology or mechanism of injury. Treatment and rehabilitation were the most popular themes, both discussed in 80% of the videos. Among the subthemes, imaging and operative were the most popular subthemes discussed, discussed in 50% and 58% of the videos, respectively. Conclusion: As YouTube is one of the most popular platforms on the Internet, this study assessed the YouTube videos regarding PHFs and their treatment. This study found the PHF videos to cover diverse topics and to be relevant to both patients and health-care professionals. Hence, they can serve as a valuable resource for patients to supplement information they receive from their care provider. However, as YouTube is a largely unregulated platform, there is a need to advocate for content creation from credible sources such as health-care facilities or providers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".