Quality and Reliability of Transarterial Chemoembolization Videos on TikTok and Bilibili: Cross-Sectional Content Analysis Study
Bibliographic record
Abstract
Background: Transarterial chemoembolization (TACE) is a widely used treatment for advanced, unresectable hepatocellular carcinoma, often requiring multiple sessions for optimal efficacy. TikTok and Bilibili have gained widespread popularity as easily accessible sources of health information. Objective: This study aims to assess the quality of the information in Chinese short videos on TACE shared on TikTok and Bilibili. Methods: In November 2024, the top 100 TACE-related Chinese-language short videos on TikTok and Bilibili (a total of 200 videos) were assessed and reviewed. Initially, basic information about the videos was recorded and analyzed. Subsequently, Global Quality Score and the DISCERN tool were used to evaluate the information quality and reliability of the videos on both platforms. Finally, multifactorial analysis was used to identify potential factors influencing the quality of the videos. Results: TikTok is more popular than Bilibili, despite its videos being shorter in length (P<.001). The quality of short videos on TACE found on both platforms was of low quality, with average Global Quality Score scores of 2.31 (SD 0.81) on TikTok and 2.48 (SD 0.80) on Bilibili, as well as DISCERN scores of 1.86 (SD 0.40) on TikTok and 2.00 (SD 0.44) on Bilibili. The number of saves (β=.184, P=.008; β=.176, P=.01) and days (β=.214, P=.002; β=.168, P=.01) since publication were identified as closely related variables to video quality and reliability. Furthermore, the duration of the video was closely related to its reliability (β=.213, P=.002). Conclusions: This study indicates that the quality of TACE-related health information in the top 100 short videos on both Bilibili and TikTok platforms is suboptimal. Patients should exercise caution when relying on health-related information from these platforms. Social media companies should establish review teams with basic medical knowledge. It is essential for the platforms to enhance their recommendation algorithms and implement measures for video quality assessment. Health care professionals should be aware of the limitations of these videos and work to improve their quality.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".