Investigating Topical Steroid Withdrawal Videos on TikTok: Cross-Sectional Analysis of the Top 100 Videos
Bibliographic record
Abstract
BACKGROUND: Social media platforms like TikTok are a very popular source of information, especially for skin diseases. Topical steroid withdrawal (TSW) is a condition that is yet to be fully defined and understood. This did not stop the hashtag #topicalsteroidwithdrawal from amassing more than 600 million views on TikTok. It is of utmost importance to assess the quality and content of TikTok videos on TSW to prevent the spread of misinformation. OBJECTIVE: This study aims to assess the quality and content of the top 100 videos dedicated to the topic of TSW on TikTok. METHODS: This observational study assesses the content and quality of the top 100 videos about TSW on TikTok. A total of 3 independent scoring systems: DISCERN, Journal of the American Medical Association, and Global Quality Scale were used to assess the video quality. The content of the videos was coded by 2 reviewers and analyzed for recurrent themes and topics. RESULTS: This study found that only 10.0% (n=10) of the videos clearly defined what TSW is. Videos were predominantly posted by White, middle-aged, and female creators. Neither cause nor mechanism of the disease were described in the videos. The symptoms suggested itching, peeling, and dryness which resembled the symptoms of atopic dermatitis. The videos fail to mention important information regarding the use of steroids such as the reason it was initially prescribed, the name of the drug, concentration, mechanism of usage, and method of discontinuation. Management techniques varied from hydration methods approved for treatment of atopic dermatitis to treatment options without scientific evidence. Overall, the videos had immense reach with over 200 million views, 45 million likes, 90,000 comments, and 100,000 shares. Video quality was poor with an average DISCERN score of 1.63 (SD 0.56)/5. Video length, total view count, and views/day were all associated with increased quality, indicating that patients were interacting more with higher quality videos. However, videos were created exclusively by personal accounts, highlighting the absence of dermatologists on the platform to discuss this topic. CONCLUSIONS: The videos posted on TikTok are of low quality and lack pertinent information. The content is varied and not consistent. Health care professionals, including dermatologists and residents in the field, need to be more active on the topic, to spread proper information and prevent an increase in steroid phobia. Health care professionals are encouraged to ride the wave and produce high-quality videos discussing what is known about TSW to avoid the spread of misinformation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".