Evaluation of Douyin Short Videos on Mammography in China: Quality and Reliability Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».