Evaluation of Douyin Short Videos on Mammography in China: Quality and Reliability Analysis
Bibliographic record
Abstract
Background: Breast cancer is the most common malignant tumor and the fifth leading cause of cancer death worldwide, imposing a significant disease burden in China. Mammography is a key method for breast cancer screening, particularly for early diagnosis. Douyin, a popular social media platform, is increasingly used for sharing health information, but the quality and reliability of mammography-related videos remain unexamined. Objective: This study aimed to evaluate the information quality and reliability of mammography videos on Douyin. Methods: In October 2023, a search using the Chinese keywords for "mammography" and "mammography screening" was conducted on Douyin. From 200 retrieved videos, 136 mammography-related videos were selected for analysis. Basic video information, content, and sources were extracted. Video content was assessed for comprehensiveness across 7 categories: conception, examination process, applicable objects, precautions, combined examinations, advantages, and report. Completeness was evaluated using a researcher-developed checklist, while reliability and quality were measured using 2 modified DISCERN (mDISCERN) tool and the Global Quality Score (GQS). Correlations between video quality and characteristics were also examined. Results: Among the video sources, 82.4% (112/136) were attributed to health professionals, and 17.6% (24/136) were attributed to nonprofessionals. Among health professionals, only 1 was a radiologist. Overall, 77.2% (105/136) of the videos had useful information about mammography. Among the useful videos, the advantages of mammography were the most frequently covered topic (53/105, 50.5%). Median values for the mDISCERN and GQS evaluations across all videos stood at 2.5 (IQR 1.63-3) and 2 (IQR 1-2), respectively. Within the subgroup assessment, the median mDISCERN score among the useful and professional groups stood at 2 (IQR 2-3) and 3 (IQR 2-3), respectively, surpassing the corresponding score for the unhelpful and nonprofessional groups at 0 (IQR 0-0) and 0 (IQR 0-0.75; P<.001). Likewise, the median GQS among the useful and professional groups was evaluated at 2 (IQR 1.5-2) and 2 (IQR 1-2), respectively, eclipsing that of the unhelpful and nonprofessional groups at 1 (IQR 1-1) and 1 (IQR 1-1.37; P<.001). The GQS was weak and negatively correlated with the number of likes (r=-0.24; P=.004), comments (r=-0.29; P<.001), and saves (r=-0.20; P=.02). The mDISCERN score was weak and negatively correlated with the number of likes (r=-0.26; P=.002), comments (r=-0.36; P<.001), saves (r=-0.22; P=.009), and shares (r=-0.18; P=.03). Conclusions: The overall quality of mammography videos on Douyin is suboptimal, with most content uploaded by clinicians rather than radiologists. Radiologists should be encouraged to create accurate and informative videos to better educate patients. As Douyin grows as a health information platform, stricter publishing standards are needed to enhance the quality of medical content.
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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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".