An Analysis of Information Sources of YouTube Videos Pertaining to Tattoo Removal: Cross-sectional Study
Bibliographic record
Abstract
Background: The American Academy of Dermatology and the Food and Drug Administration recommend consultation with a dermatologist prior to undergoing laser tattoo removal. However, non-health care professionals offer tattoo removal. Understanding the information available on the internet for patients regarding tattoo removal is important given that individuals are increasingly consulting digital sources to make decisions regarding skin care. Prior research has identified that YouTube contains misinformation on dermatologic health. Objective: Here, we present a cross-sectional study that determined the sources of information in YouTube videos that discuss tattoo removal and described the content presented to viewers. Methods: Using the query "tattoo removal," we reviewed English-language YouTube videos that explicitly discussed tattoo removal. The following data were recorded: profession of the presenter, tattoo removal method discussed, whether an explicit recommendation to see a dermatologist or physician was present in the video, and number of views. Results: We analyzed 162 YouTube videos. We found that the majority were presented by non-health care professionals (n=125, 77%), with only 4 (3.7%) records of this subset recommending viewers to seek consultation from a dermatologist to ensure safe and adequate tattoo removal. Conclusions: Based on our findings, we recommend that dermatologists and other health care professionals provide high-quality, evidence-based information to viewers on tattoo removal and encourage dermatology societies to share via their social media platforms information about the importance of consulting a dermatologist for tattoo removal.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".