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Record W4401793868 · doi:10.1097/jw9.0000000000000174

Short and clear: YouTube Shorts recommendations for rosacea

2024· article· en· W4401793868 on OpenAlexaboutno aff
Michelle Ko, Emily Newsom

Bibliographic record

VenueInternational Journal of Women’s Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsRosaceaInternet privacyComputer scienceMedicineDermatology

Abstract

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What is known about this subject in regard to women and their families? Rosace is a common disease, estimated to affect over 16 million people in the United States including women. Rosacea is categorized into 5 subtypes (erythematotelangiectatic, papulopustular, phymatous, ocular, and other variants), each associated with different treatment approaches. In an era where social media is increasingly used by patients as a source of health information, it is important to understand what treatment recommendations patients and their children may be receiving through these avenues. What is new from this article as messages for women and their families? Although previous studies have focused on YouTube and rosacea, none so far have done so using YouTube Shorts, a new short-form video platform only released in March 2021. In this study, we conducted a cross-sectional analysis to assess what recommendations are suggested for rosacea in YouTube Shorts. The sparse number of YouTube Shorts videos on rosacea highlights the need for dermatologists to meet patients where they are to educate, combat misinformation, and maintain their status as experts in skin disease. Women, especially the younger demographic and teen girls tend to be a targeted audience for skin care videos. Introduction A recent study found that 87.6% of YouTube users used the platform to watch health-related content.1 Thus, it is important to understand what health recommendations are being offered to the public, especially for common diseases such as rosacea,2 a cutaneous disorder estimated to affect over 16 million people in the United States. Although previous studies have focused on YouTube and rosacea,3 none so far have done so using YouTube Shorts, a short-form video platform released in March 2021. We conducted a cross-sectional analysis to assess what recommendations are suggested for rosacea in YouTube Shorts. Methods On January 12, 2023, we searched YouTube.com for the following: #shorts + rosacea treatment. The search was sorted through YouTube by “relevance.” “Incognito mode” was used to minimize personal search algorithm influences. Author M.K. reviewed the first 150 YouTube Shorts videos to appear and excluded videos that were duplicates, not relevant, not in English, or had less than 1000 views. Publicly available metrics were recorded, and an engagement value was determined by the following equation: [(likes + comments)/views] × 100. Videos were categorized by creator type and recommendations were recorded. Videos were categorized by the rosacea subtype the treatment was intended for (erythematotelangiectatic, papulopustular, phymatous, ocular, and other variants).4 Recommendations were categorized by those that (1) can be carried out by patients independently, (2) require prescription or procedure, and (3) are home remedies.4 This study was modeled after research by Nickles et al.5 Results Of the 150 videos collected, only 40 met the inclusion criteria with the majority (n = 76) excluded because they were duplicates. The majority of videos featured dermatologists (65%), with the minority featuring skin care companies (5%) (Table 1). Most video creators were based in the United States (65%), United Kingdom (15%), and India (10%), while the rest were based in Canada (5%), Korea (2.5%), and Malaysia (2.5%). The top recommendation mentioned by videos was laser or light therapy (32.50%) (Table 2). Many videos did not specify which rosacea subtype their recommendations were for (47.50%). Most videos (72.50%) had recommendations that can be carried out by patients independently, such as trigger avoidance, skin care habits, and over-the-counter (OTC) medications. Examples of OTC medication included 10% azelaic acid and skin products with heparan sulfate, hyaluronic acid, niacinamide, retinol, and vitamin C. Interestingly, 2 videos recommended using OTC nasal oxymetazoline spray, traditionally used as an intranasal decongestant, as a topical to vasoconstrict prominent telangiectatic vessels. Additionally, 5 videos mentioned using green-tinted cosmetics to disguise erythema. Less than half of the videos had recommendations requiring medical intervention including prescription medications, such as 15% to 20% azelaic acid, topical metronidazole, and topical ivermectin, and in-office procedures such as laser and light therapy (42.50%). A minority of videos (15%) mentioned home remedies, such as topical pennywort, mugwort, argan oil, licorice extract, green tea, and skin care techniques, such as the use of ice rollers, ice globes, and “face cups.” Table 1 - Descriptive characteristics of YouTube Shorts recommendations for rosacea Video speaker n % Mean views Mean likes Mean comments Mean engagement Video specifies treatment for erythematotelangiectatic rosaceaa Video specifies treatment for phymatous rosacea Video with no subtype specified Recommend OTC medications or skin care products or lifestyle changes Recommend prescription medication or procedure Recommend home remedies All 40 41,823 1066 25.3 2.44% 20 (50%) 1 (2.5%) 19 (47.5%) 29 (72.5%) 17 (42.5%) 6 (15.0%) Dermatologistb 26 65.00% 44,819 1303 32.19230769 2.86% 15 (57.69%) 1 (3.85%) 10 (38.46%) 18 (69.23%) 12 (46.15%) 2 (7.69%) Patient 8 20.00% 50,538 725 16 1.47% 2 (25.00%) 0 (0.00%) 6 (75.00%) 8 (100%) 3 (37.50%) 0 Skin care groupc 4 10.00% 5100 73 7 1.54% 3 (75.00%) 0 (0.00%) 1 (25.00%) 2 (50%) 2 (50.00%) 0 Skin care company 2 5.00% 41,450 1338 8 2.63% 0 (0.00%) 0 (0.00%) 2 (100.00%) 2 (100%) 0 (0.00%) 2 (100%) OTC, over-the-counter.aDetermined videos that mentioned “red,” “flushing,” or “telangiectasias.”bTwenty-one featured US-based, board-certified physicians (confirmed by the American Board of Medical Specialties website). Five featured non-US dermatologists (self-reported).cChannels self-identified as skin, laser, and cosmetic dermatology group practices. Table 2 - Summary of the top 13 recommended rosacea treatments in the YouTube short videos analyzed Top 13 recommendations for rosacea on YouTube Shorts (N = 40) No. of videos with the recommendation % of total videos Lasers or light therapy 13 32.5% Trigger avoidancea 9 22.5% Skincare habitsb 7 17.5% Sunscreen 7 17.5% 10% azelaic acid 6 15.0% Green-tinted sunscreen or makeup 5 12.5% Topical metronidazole 4 10.0% Topical niacinamide 3 7.5% Topical sulfur 3 7.5% 15% azelaic acid 2 5.0% Topical ivermectin 2 5.0% Topical oxymetazoline nasal spray 2 5.0% Topical Vitamin C 2 5.0% aExamples of triggers included alcohol, coffee, emotions/stress, extreme exercise, extreme temperatures, red wine, sugar, spicy foods, sun, and wind.bExamples of skincare habits included the use of gentle cleansers and sunscreen. Conclusion Short-form social media content, including YouTube Shorts, is rapidly increasing in popularity. While many YouTube Shorts videos on rosacea had useful information, there were several that recommended treatments with questionable efficacy. This misinformation has the potential to cost patients money and time and prolong or worsen symptoms by causing delay in seeking care. The sparse number of YouTube Shorts videos on rosacea highlights the need for dermatologists to meet patients where they are to educate, combat misinformation, and maintain their status as experts in skin disease. Conflicts of interest None. Funding None. Study approval N/A Author contributions MK and EN contributed to the study conception and design. MK performed material preparation, data collection, and analysis and wrote the first draft of the manuscript. All authors commented on previous versions of the manuscript and read and approved the final manuscript.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.341
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations2
Published2024
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