Evidence map›Paper›PMID 41639252›Full record

ArticleScientific reports2026

Evaluating the information quality and reliability of ovarian cancer educational content on TikTok and Bilibili: a cross-sectional study.

Tao Sun, Muyuan Guo, Ruping Zhao, Xiaoyu Tang, Yaling Zhang, Yuling Zheng

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Tao Sun *Department of Oncology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Muyuan Guo *Henan University of Chinese Medicine, Zhengzhou, China.
Ruping ZhaoHenan University of Chinese Medicine, Zhengzhou, China.
Xiaoyu TangDepartment of Oncology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Yaling ZhangDepartment of Oncology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Yuling ZhengDepartment of Oncology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China. zhengyl@hactcm.edu.cn.

Funding

Henan Province Natural Science Foundation Project (No. 242300420435)Henan Traditional Chinese Medicine Research Special Project (No. 2023ZY2018)The 4th National Chinese Medicine Expert Inheritance Studio Project (No. [2022]75)
6 · The paper itself

Abstract

Ovarian cancer is the gynecological malignancy with the highest mortality rate. In the digital era, short videos have become a primary means for the public to access information about ovarian cancer. We aim to evaluate the quality, reliability and content integrity of ovarian cancer-related videos on TikTok and Bilibili. Searches for the keyword “卵巢癌” (ovarian cancer) on TikTok and Bilibili platforms recorded uploader types, content categories, and user engagement metrics. Videos were evaluated using the Global Quality Scale (GQS), modified DISCERN assessment tool (mDISCERN), and Content Integrity Score (CS). Higher GQS scores and mDISCERN scores indicate higher video quality and reliability, respectively. Statistical analysis employed nonparametric tests and Spearman’s correlation analysis. A total of 213 videos were included and analyzed (TikTok: n = 126; Bilibili: n = 87). Regarding general information, Bilibili videos had a significantly longer median duration than TikTok videos (250 s vs. 97 s, p < 0.001), but user engagement metrics were significantly lower on the Bilibili platform (p < 0.001). Regarding assessment of quality, reliability and content integrity, no statistically significant differences were observed between platforms in GQS, mDISCERN, or CS scores (p > 0.05). Overall quality and reliability were poor (88.73% of videos scored ≤ 3 on GQS, 86.86% scored ≤ 2 on mDISCERN), with insufficient completeness (76.06% scored low on CS). Both specialists and non-specialists physicians achieved significantly higher GQS scores than general users (p < 0.05). Correlation analysis revealed no positive relationship between user engagement metrics and any scores (GQS, mdiscern, and CS). TikTok and Bilibili exhibit significant differences in ovarian cancer-related short video content and user engagement metrics, yet the quality of information on both platforms falls short of ideal standards. Furthermore, user interaction levels did not differ based on assessment scoring (quality, reliability or content integrity). We urge platforms to optimize their health content recommendation mechanisms, encourage healthcare professionals to create high-quality, accessible content, and recommend that clinicians guide patients in critically evaluating information from short videos.

Indexed as

Ovarian NeoplasmsVideo RecordingCross-Sectional StudiesDigital MediaFemaleHumansReproducibility of ResultsBilibiliInformation qualitymDISCERNOvarian cancerReliabilityShort videoTikTok

Identifiers

PMID41639252
PMCPMC12923753

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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.