Quality Assessment of Radiotherapy Health Information on Short-Form Video Platforms of TikTok and Bilibili: Cross-Sectional Study
Bibliographic record
Abstract
Background: Radiotherapy (RT) is a crucial modality in cancer treatment. In recent years, the rise of short-form video platforms has transformed how the public accesses medical information. TikTok and Bilibili, as leading short-video platforms, have emerged as significant channels for disseminating health information. However, there is an urgent need to evaluate the quality and reliability of the information related to RT available on these platforms. Objective: This study aims to systematically assess the information quality and reliability of RT-related short-form videos on TikTok and Bilibili platforms using the Global Quality Score (GQS) and a modified DISCERN (mDISCERN) evaluation tool, thereby elucidating the current landscape and challenges of digital health communication. Methods: This study systematically retrieved the top 100 RT-related videos on TikTok and Bilibili as of February 25, 2025. The quality of the videos was assessed using the GQS (1-5 points) and an mDISCERN scoring system (1-5 points). Statistical analyses were conducted using the Mann-Whitney U test, as well as Spearman and Pearson correlation analyses, to ensure the reliability and validity of the results. Results: A total of 200 short-form videos related to RT were analyzed, revealing that the overall quality of videos on TikTok and Bilibili is unsatisfactory. Specifically, the median GQS for TikTok was 4 (IQR 3-4), while for Bilibili, it was 3 (IQR 3-4). The median mDISCERN scores for both platforms were 3 (IQR 2-4 and 3-4, respectively), and no significant differences were observed between the 2 platforms regarding the GQS (P=.12) and mDISCERN score (P=.10). On TikTok, 53% (53/100) of videos had a GQS of 4 or higher ("good" quality or better). On Bilibili, 45% (45/100) of videos had an mDISCERN score of 4 or higher, indicating "relatively reliable" quality. Videos produced by professionals, institutions, and nonprofessional institutions had significantly higher mDISCERN scores than those made by patients, with statistical significance (P<.001, P<.001, and P<.01, respectively). Furthermore, the correlations between the number of bookmarks and video duration, with mDISCERN scores, were 0.172 (P=.02) and 0.192 (P=.007), respectively. However, no video variables were found to predict the overall quality and reliability of the videos effectively. Conclusions: This study revealed that the overall quality of RT-related videos on TikTok and Bilibili is generally low. However, videos uploaded by professionals demonstrate higher information quality and reliability, providing valuable support for patients seeking guidance on health care management and treatment options for cancers. Therefore, improving the quality and reliability of video content, particularly that produced by patients, is crucial for ensuring that the public has access to accurate medical information.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".