Evidence map›Paper›PMID 41742938›Full record

ArticleDigital health

Assessment of the most popular short videos about hemorrhoids on Chinese TikTok: A cross-sectional study.

Mingqiu Lu, Danna Shen, Liye Zhuang, Jiazi Yu

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. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Mingqiu LuDepartment of Pharmacy, Ningbo Medical Center Lihuili Hospital, Ningbo, China.ORCID https://orcid.org/0009-0007-6068-4992
Danna ShenDepartment of Pharmacy, Ningbo Medical Center Lihuili Hospital, Ningbo, China.ORCID https://orcid.org/0009-0007-9707-7029
Liye ZhuangDepartment of Anesthesiology, Ningbo Medical Center Lihuili Hospital, Ningbo, China.ORCID https://orcid.org/0009-0009-3360-2634
Jiazi YuDepartment of Colorectal Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, China.ORCID https://orcid.org/0000-0002-8919-1178

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Social media like TikTok serve as major channels for sharing health information on hemorrhoids. However, little is known about the quality, reliability, and impact of this content on user decisions. Objective: Our study aims to evaluate the quality and reliability of hemorrhoid-related videos on Chinese TikTok. Methods: On August 7, 2025, we systematically searched Chinese TikTok using relevant keywords and identified the 192 most-liked hemorrhoid-related videos. Two independent reviewers assessed each video using the Global Quality Score (GQS), the modified DISCERN (mDISCERN) score, and the Journal of the American Medical Association (JAMA) criteria. User engagement metrics (total likes, comments, saves, and shares), video length, and upload date were recorded. Spearman correlation analysis was performed to explore the association between quality scores and engagement metrics. Results: Overall, 87 videos were included and accumulated 6,599,153 likes. Of these, 25.29% (n = 22) were classified as low quality, 40.23% (n = 35) as moderate quality, and 34.48% (n = 30) as high quality. Most videos (74.71%, n = 65) were uploaded by certified physicians, who contributed the majority of high-quality content (93.34%), whereas low-quality content mainly originated from general users (54.54%). Videos uploaded by certified physicians demonstrated significantly higher quality and reliability than those from non-physicians ( Conclusion: The quality and reliability of hemorrhoid-related videos on Chinese TikTok are moderate. To improve health information quality, TikTok could consider extending video length as appropriate, strengthening content moderation, and encouraging creators to reference authoritative sources.

Indexed as

health informationHemorrhoidsreliabilityshort videossocial mediaTikTokvideo quality

Identifiers

PMID41742938
PMCPMC12929877

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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.