Evidence mapPaperPMID 42359149Full record

ArticleFrontiers in public health2026

"Short" is not always "scientific": cross-sectional quality assessment and machine learning-based evaluation of weight management short videos on TikTok and Bilibili.

Jinsong Du, Le Zhu, Hailing Zhou

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

3 authors.

Jinsong Du *School of Health Management, Zaozhuang University, Zaozhuang, China.
Le Zhu *School of Health Management, Zaozhuang University, Zaozhuang, China.
Hailing Zhou *School of Health Management, Zaozhuang University, Zaozhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to systematically evaluate and compare the content quality and reliability of weight management-related short videos on TikTok and Bilibili, identify key factors influencing video quality, and develop an automated quality prediction tool to support the construction of a healthier digital health communication ecosystem. Methods: A cross-sectional study design was adopted. The top 100 weight management-related videos ranked by overall relevance were collected from TikTok and Bilibili, respectively (total Results: Videos on Bilibili had significantly higher GQS scores than those on TikTok ( Conclusion: Videos published by Professional individuals possess greater academic value and practical significance. However, weight management information on short-video platforms exhibits a mismatch between popularity and quality. The automated evaluation model and online tool proposed in this study provide strong support for the public in identifying reliable scientific information and for regulatory authorities in developing intelligent governance systems.

Indexed as

Machine LearningVideo RecordingWeight Reduction ProgramsBoosting Machine Learning AlgorithmsCross-Sectional StudiesDigital HealthDigital MediaHumansReproducibility of Resultshealth communicationinformation qualitymachine learningshort videoweight management

Identifiers

PMID42359149
PMCPMC13292766

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.