Evidence map›Paper›PMID 38694685›Full record

ArticleCureus2024

Exploring Digital Dermatology: An Analysis of Ichthyosis Content on TikTok.

Magda S Wojtara, Therese M Guinto, Dorcas K Adebogun, Olivier Uwishema

Abstract read
In one paragraph

Article in Cureus, 2024. 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

4 authors.

Magda S WojtaraDepartment of Human Genetics, University of Michigan, Ann Arbor, USA.
Therese M GuintoSchool of Information Technology, Mapúa Malayan Digital College, Quezon City, PHL.
Dorcas K AdebogunSchool of Medicine, Virginia Commonwealth University, Richmond, USA.
Olivier UwishemaFaculty of Medicine, Karadeniz Technical University, Trabzon, TUR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background TikTok is among the most popular social media sites, and its utilization for health information is growing each day. The present study assesses the popularity and quality of the top 100 most-liked videos on TikTok tagged with "#ichthyosis." This study aims to do so by assessing contributions from physician, and nonphysician sources (such as from patients) to guide healthcare professionals interested in leveraging this platform for public health.  Methodology A cross-sectional analysis of the top 100 most-liked videos tagged with "#ichythosis" on TikTok as of January 3, 2024, was conducted. The parameters assessed for this study include observable characteristics, content type, and whether the creator was a physician or nonphysician. The quality of the content was measured using the DISCERN scale.  Results Based on these results, 14 of the top 100 videos were posted by physicians with 1,912,975 as the mean number of views. There were 86 videos posted by nonphysician creators averaging 2,675,341 views. Videos posted by nonphysician creators had a higher average number of views, number of likes, and number of comments but less average saves. Videos made by physicians and educational content had the highest average DISCERN scores, whereas nonphysician, awareness, and personal experience content had the lowest average DISCERN scores.  Conclusions Physicians are deemed trustworthy, reliable sources of healthcare-related information on TikTok. This study emphasizes the importance of physicians continuing to provide reliable, evidence-based health information on social media platforms such as TikTok.

Indexed as

ichthyosispatient-centeredskin disease/dermatologysocial media educationtiktok

Identifiers

PMID38694685
PMCPMC11061813

What OpenQuestion holds

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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.