Evidence map›Paper›PMID 40918844›Full record

ArticleCureus2025

Quality and Reliability of TikTok Videos on Uterine Fibroid Embolizations: An Observational Analysis of Internationally Trending Social Media Videos.

Shreya Katwala, Ashna Chali, Michelle Wu, Felix Yang, Mira Malavia, Trung Pham

Abstract read
In one paragraph

Article in Cureus, 2025. 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

6 authors.

Shreya KatwalaInternal Medicine, University of Missouri Kansas City School of Medicine, Kansas City, USA.
Ashna ChaliInternal Medicine, University of Missouri Kansas City School of Medicine, Kansas City, USA.
Michelle WuInternal Medicine, University of Missouri Kansas City School of Medicine, Kansas City, USA.
Felix YangAnesthesia, Critical Care, and Pain Medicine, University of Missouri Kansas City School of Medicine, Kansas City, USA.
Mira MalaviaInterventional Radiology, University of Missouri Kansas City School of Medicine, Kansas City, USA.
Trung PhamInternal Medicine, University of Missouri Kansas City School of Medicine, Kansas City, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTikTok has emerged as a popular platform for sharing medical insights, but concerns exist regarding disseminating inaccurate information on medical conditions, potentially harming patient care. This study aims to evaluate the quality and reliability of TikTok videos on uterine fibroid embolization (UFE). It also examines how video engagement and content quality vary based on the uploader type and video style. METHODOLOGY: We selected the top 100 TikTok videos on UFEs based on the number of likes since March 2024. These videos were identified using the hashtag "#UFE." Videos were categorized based on several factors, including the number of likes, comments, shares, upload date, uploader's background (academic, non-healthcare professionals (non-HCPs), non-physician, non-radiologist, and radiologist), and type of content (anecdotal, educational, and procedural). The strength of treatment recommendations was assessed for all videos using the DISCERN instrument (16 to 80), which assesses reliability of treatment information, and the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V), which assesses understandability and actionability via a percentage score. Descriptive and ANOVA analyses were conducted with a statistical significance set at p < 0.05.

resultsOf the top 100 TikTok videos related to UFE, 99 were in English and one was in Spanish, which was excluded from our study. A total of 12 videos (12.12%) were uploaded by radiologists, while the majority (66, 66.67%) were created by non-HCPs. Non-radiologist physicians garnered the highest average engagement and quality scores, including the highest DISCERN score (41.07). Academic institutions achieved the highest PEMAT understandability score (80.47%). Non-HCPs ranked second in engagement but had the second-lowest PEMAT understandability score (65.96%), just above non-physician HCPs, who had the lowest score (64.68%). Educational videos (35, 35.35%) outperformed anecdotal ones (59, 59.59%) in quality, achieving higher DISCERN (40.57) and PEMAT understandability (68.85%) scores. While DISCERN scores did not significantly differ by video aim, both PEMAT understandability (p = 0.01763) and DISCERN (p = 0.00166) scores showed significant differences based on the uploader type. DISCUSSION: Our analysis of the top 100 TikTok videos on UFE reveals a landscape dominated by non-physician contributors, with only 12 videos created by radiologists. Despite this, content from non-radiologist physicians garnered the highest engagement and exhibited the highest quality, underscoring the influence of credible medical voices on social media. While anecdotal content prevails, educational videos achieved better quality scores, highlighting the value of evidence-based communication. These findings suggest a critical opportunity for radiologists and other physicians to enhance patient education, counter misinformation, and leverage TikTok as a low-cost, high-impact platform for healthcare communication. However, limitations include a narrow focus on highly liked content and exclusion of TikTok's algorithmic influence.

Indexed as

evidence-based clinical practicemedical misinformationmultiple uterine fibroidspatient educationsocial media analyticstiktokuterine fibroid embolizationvideo content analysis

Identifiers

PMID40918844
PMCPMC12409774

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.