Alopecia areata and pattern hair loss (androgenetic alopecia) on social media – Current public interest trends and cross‐sectional analysis of <scp>YouTube</scp> and <scp>TikTok</scp> contents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: With an ever-growing influence of social media in healthcare, concurrent with increased emphasis on patient autonomy and shared decision-making, dermatologists treating hair loss need to be cognizant of online interest trends and the types of information disseminated across popular platforms. OBJECTIVES: To evaluate recent health-related interest trends and assess engagement, quality, and accuracy of alopecia areata (AA) and pattern hair loss (PHL, androgenetic alopecia) contents on social media. METHODS: Relative search volumes (RSVs) were extracted from Google Trends using the search category 'alopecia areata' and 'pattern hair loss'. Eighty matching videos on TikTok and YouTube were also extracted and characterized. Viewer engagement was estimated using the engagement ratio, and quality and accuracy were assessed using DISCERN and Dy et al. Accuracy Scale (DAS). CONCLUSIONS: AA-related contents on TikTok discussing personal experiences of female subjects were significantly more engaging. DISCERN and DAS scoring showed significantly higher quality and accuracy in videos created by healthcare providers on YouTube, but not TikTok, which could in part be related to YouTube videos being longer. RSV fluctuations corresponding to news in popular culture had high impact. Sponsorship disclosures were generally not reported in product promotional videos.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it