Evidence map›Paper›PMID 41429799›Full record

ArticleScientific reports2025

Quality and dissemination of thyroid cancer information on short-video platforms a cross-platform evaluation.

Xiaoran Zheng, Qiankuan Li, Lu Jin, Kaihang Shi

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

4 authors.

Xiaoran Zheng *Department of Internal Medicine, Handan Fukang Hospital, Handan, 056003, Hebei, China. 15233626599@163.com.ORCID 0000-0002-8622-1685
Qiankuan Li *Department of Traditional Chinese Medicine, Handan Fukang Hospital, Handan, 056003, Hebei, China.
Lu JinZhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Guangdong, 528400, China.
Kaihang ShiDepartment of Dermatology and Venereology, Affiliated Hospital of Chengde Medical University, Hebei, 067000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Short-video platforms are becoming major sources of health information, but the quality and dissemination patterns of thyroid cancer-related content remain unclear. We conducted a large-scale cross-sectional analysis of 1,248 thyroid carcinoma videos retrieved from TikTok, Kwai, and Rednote between February 5-10, 2025. Video characteristics, including source, duration, and engagement metrics, were extracted, and quality was evaluated using JAMA, GQS, mDISCERN and PEMAT. TikTok videos had longer duration, higher engagement, and overall better quality compared with those on Kwai and Rednote. Videos from healthcare professionals and institutions achieved significantly higher information quality, whereas independent creators generated greater audience interaction, particularly comments and shares. Knowledge-focused content showed the higher reliable, while patient vlogs attracted more engagement. Engagement indicators were strongly intercorrelated but exhibited weak or negative associations with quality, suggesting that popularity does not fully reflect informational rigor. These findings highlight a divergence between video quality and audience reach, underscoring the need for professional participation, credential verification, and algorithmic refinement to enhance the visibility of evidence-based content and support public health literacy.

Indexed as

Information DisseminationThyroid NeoplasmsVideo RecordingCross-Sectional StudiesHumansHealth communicationInformation qualityShort videoThyroid cancerUser engagement

Identifiers

PMID41429799
PMCPMC12722304

What OpenQuestion holds

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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.