Assessing the quality of general anesthesia-related short videos on TikTok: a cross-sectional study

BACKGROUND: TikTok has become a significant source of health information for the public, yet misinformation on the platform may adversely affect health decision-making. To date, no study has systematically evaluated the quality of general anesthesia-related short videos on TikTok. OBJECTIVE: This study aims to assess the quality and reliability of general anesthesia-related videos on TikTok using validated instruments and to examine correlations among video source, duration, user engagement, and content quality. METHODS: We conducted a cross-sectional analysis of TikTok videos retrieved using the keyword "" (general anesthesia). Video quality was evaluated using the Global Quality Scale (GQS), modified DISCERN (mDISCERN), JAMA benchmark criteria, and a custom composite score assessing risk disclosure, procedural completeness, and accuracy. Uploaders were categorized by professional background. Statistical analyses included Holm-Bonferroni corrected non-parametric tests, Spearman correlations, and multivariable ordinal logistic regression adjusting for video duration. RESULTS: Of 150 retrieved videos, 127 met the inclusion criteria. Overall quality was low-to-moderate: median GQS 2.0 (IQR: 1.0-3.0), JAMA 2.0 (1.0-2.0), mDISCERN 2.0 (2.0-2.0), and custom score 4.0 (3.0-4.0), with notably poor risk disclosure (median 0.0). Videos from anesthesia professionals scored highest across all metrics. Engagement metrics did not correlate with video quality. Video duration correlated positively only with GQS (r = 0.339, P < 0.001). Multivariable analysis confirmed that uploader background independently predicted quality; patients and the general public had significantly lower odds of producing high-quality content compared with anesthesia professionals (P < 0.001). CONCLUSIONS: General anesthesia-related videos on TikTok exhibit substantial quality deficiencies, particularly in risk communication. Video popularity does not reflect scientific reliability. Given that professional background independently predicts content quality, healthcare professionals should actively create evidence-based content, and platforms must algorithmically prioritize authoritative sources to mitigate misinformation.