TikTok and Orthopaedic Education: Engaging the Next Generation of Patients
Bibliographic record
Abstract
INTRODUCTION: Social media is increasingly pivotal in healthcare communication, with TikTok emerging as a leading platform because of its visually engaging, short-format videos. With nearly 2 billion users spending over 50 minutes daily on the app, TikTok offers a novel medium for disseminating orthopaedic information. Pediatric fractures-employed here as a representative model-are particularly relevant given the high social media use among youth and caregivers. This study investigates TikTok's role as an educational tool for orthopaedic surgeons and examines public engagement. METHODS: A cross-sectional study was conducted analyzing TikTok videos related to pediatric fractures. A newly created TikTok account was used to identify the top popular videos for each fracture type, excluding non-English, off-topic, private, or duplicate content. Engagement metrics, including views, likes, shares, comments, and bookmarks, were recorded. Video understandability was assessed using the Patient Education Materials Assessment Tool for Audiovisual Materials. Video reliability was assessed using the modified DISCERN scale. Videos were categorized by creator identity (physician, allied healthcare provider, patient, parent, and other). RESULTS: A total of 190 TikTok videos accumulated more than 25.7 million views and 1.8 million likes. Although nonexpert sources (parents and patients) contributed 87.9% of the content, healthcare professional-generated videos comprised 12.1% and demonstrated markedly higher engagement and quality scores. In particular, physician-produced content achieved the highest median views and shares (P < 0.01). DISCUSSION: The predominance of nonexpert content underscores a missed opportunity to leverage TikTok for disseminating reliable, evidence-based orthopaedic education. Expert-driven videos not only offer superior clarity, reliability, and actionable guidance but also align with the public's preference, suggesting broad applicability across orthopaedic subspecialties. CONCLUSION: TikTok represents a promising platform for enhancing orthopaedic education. Increasing healthcare professional engagement may improve the delivery of accurate, evidence-based content, ultimately advancing musculoskeletal health literacy and patient outcomes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 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".