Evidence map›Paper›PMID 41381617›Full record

ArticleScientific reports2025

Information quality assessment and user engagement prediction of short videos about diabetic kidney disease on TikTok and bilibili.

Shuo Lin, Jianjie Ju, Zhouhua Wang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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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

8 citing papers in PubMed.

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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

3 authors.

Shuo Lin *Mindong Hospital Affiliated to Fujian Medical University, Ningde, 355000, Fujian, People's Republic of China.
Jianjie Ju *School of Pharmacy, Fujian Medical University, University Town, No. 1 Xuefu North Road, Fuzhou, 350122, People's Republic of China.
Zhouhua WangMindong Hospital Affiliated to Fujian Medical University, Ningde, 355000, Fujian, People's Republic of China. wangzh9065@163.com.

Funding

Fujian Medical University 2019QH1217Fujian Provincial Health Commission Scientific Research Plan Project 2021QNA076
6 · The paper itself

Abstract

With the growing availability of short video platforms such as TikTok and Bilibili, patients with diabetic kidney disease (DKD) are increasingly seeking health information through these channels. However, the quality and user engagement of DKD-related content on these platforms have not been systematically evaluated. This exploratory cross-sectional study assessed the quality and reliability of DKD-related short videos and examined predictors of user engagement. On April 4, 2025, the top 100 DKD-related videos were collected from each platform. Content quality and reliability were assessed using the Global Quality Score (GQS), the modified DISCERN (mDISCERN), and the Medical Quality Video Evaluation Tool (MQ-VET). An eXtreme Gradient Boosting (XGBoost) model was employed to predict the number of likes and identify associated predictors. Despite being shorter in length, TikTok videos received significantly more likes, saves, shares, and comments than those on Bilibili (all p < 0.001), and scored higher on GQS and MQ-VET, with no significant difference in mDISCERN scores. Videos uploaded by professionals generally showed higher quality. Follower count, video length, and days since upload were the strongest predictors of engagement. Overall, TikTok videos exhibited higher quality and engagement than those on Bilibili; however, given the algorithm-driven sampling, uploader-level clustering, and tool adaptation, these findings should be interpreted as descriptive and exploratory rather than causal.

Indexed as

Diabetic NephropathiesVideo RecordingCross-Sectional StudiesHumansReproducibility of ResultsBilibiliDiabetic kidney diseaseInformation qualityTikTokXGBoost

Identifiers

PMID41381617
PMCPMC12698846

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LicenceCC BY-NC-ND
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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.