The Role of TikTok in Education on Hidradenitis Suppurativa in Skin of Color: Cross-Sectional Analysis
Bibliographic record
Abstract
BACKGROUND: TikTok serves as a major source of information. Most content on common skin conditions comes from patients rather than clinicians. Hidradenitis suppurativa disproportionately affects Black women and faces frequent misdiagnosis and delayed care. Understanding content sources and messaging on TikTok remains important. OBJECTIVE: This study aimed to describe who produces HS TikTok content featuring Black skin and to quantify the treatments, products, and themes presented. METHODS: TikTok was searched using the phrase "hidradenitis suppurativa in black skin." Fifty videos were reviewed by a single evaluator. Data collected included creator type, board certification status, themes, products mentioned, and commercial involvement such as paid sponsorships or sales commissions. RESULTS: Among 50 videos, patients produced 24 (48%). Board-certified dermatologists produced 10 (20%) videos, and board-certified plastic surgeons produced seven (14%). Other creators, including nurse practitioners, beauty service providers, and individuals with unclear credentials, accounted for nine videos (18%). Seven videos included products linked to sales commissions, and two involved paid sponsorships. Treatment-related content dominated, appearing in 35 videos (70%). PanOxyl and Hibiclens were the most frequently mentioned products, appearing in five (10%) and six (12%) videos. Fifteen videos (30%) focused on explaining hidradenitis suppurativa, with nine created by healthcare professionals and six by patients. Remaining content emphasized lived experience and dietary approaches. CONCLUSIONS: Patients dominate experiential and lifestyle-focused hidradenitis suppurativa content on TikTok, while physicians primarily provide treatment education. Limited dermatologist representation contributes to gaps between medical guidance and patient-driven narratives. Greater participation by dermatologists on TikTok offers an opportunity to address misinformation, discuss product safety, and provide evidence-based guidance alongside patient experiences.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".