Evidence map›Paper›PMID 41800161›Full record

ArticleDigital health

Quality, engagement, and predictive validity of acne-related short videos on Chinese platforms Bilibili and TikTok: A cross-sectional content analysis.

Yuhan Xie, Qinxiao Li, Wenmin Deng, Yuxin Yan, Longmei Duan, Yuting Chen, Yusheng Wan, Kainian Han, Heni Ma, Yan Zheng

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Yuhan XieDepartment of Dermatology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.ORCID https://orcid.org/0009-0000-1569-2861
Qinxiao LiDepartment of Dermatology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Wenmin DengXi'an Jiaotong University, Xi'an, China.
Yuxin YanXi'an Jiaotong University, Xi'an, China.
Longmei DuanDepartment of Dermatology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Yuting ChenXi'an Jiaotong University, Xi'an, China.
Yusheng WanXi'an Jiaotong University, Xi'an, China.
Kainian HanXi'an Jiaotong University, Xi'an, China.
Heni MaXi'an Jiaotong University, Xi'an, China.
Yan ZhengDepartment of Dermatology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the informational quality and user engagement of acne-related videos on Bilibili and TikTok, and examine associations with uploader characteristics, disease-related topics, presentation formats, and factors linked to high-quality content. Methods: A cross-sectional analysis was conducted on 272 videos (122 from Bilibili, 150 from TikTok) retrieved in May 2025. Video characteristics, uploader types, disease-related topics, and presentation formats were recorded. Quality was assessed using the Results: Bilibili videos were longer (median 409 s vs 51 s; Conclusions: Acne-related videos on Bilibili and TikTok were generally of suboptimal quality. Bilibili favored coherence and accuracy, while TikTok favored transparency and engagement. Quality assessments outperformed engagement metrics in identifying high-quality content. These findings highlight the need to improve credentialing and promote engaging, evidence-based formats to enhance the reliability and impact of dermatologic information on short-video platforms.

Indexed as

Acne vulgarispredictive validityshort-video platformsuser engagementvideo quality

Identifiers

PMID41800161
PMCPMC12961113

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.