Tetrahydrocannabinol and Skin Cancer: Analysis of YouTube Videos
Bibliographic record
Abstract
BACKGROUND: Cannabis oil is being used topically by patients with skin cancer as a homeopathic remedy, and has been promoted and popularized on social media, including YouTube. Although topical cannabinoids, especially tetrahydrocannabinol (THC), may have antitumor effects, results from a sparse number of clinical trials and peer-reviewed studies detailing safety and efficacy are still under investigation. OBJECTIVE: We sought to assess the accuracy, quality, and reliability of THC oil and skin cancer information available on YouTube. METHODS: The 10 most-viewed videos on THC oil and skin cancer were analyzed with the Global Quality Scale (GQS), DISCERN score, and useful/misleading criteria based on presentation of erroneous and scientifically unproven information. The videos were also inspected for source, length, and audience likes/dislikes. Top comments were additionally examined based on whether they were favorable, unfavorable, or neutral regarding the video content. RESULTS: All analyzed videos (10/10, 100%) received a GQS score of 1, corresponding to poor quality of content, and 9/10 (90%) videos received a DISCERN score of 0, indicating poor reliability of information presented. All 10 videos were also found to be misleading and not useful according to established criteria. Top comments were largely either favorable (13/27, 48%) or neutral (13/27, 48%) toward the content of the videos, compared to unfavorable (1/27, 4%). CONCLUSIONS: Dermatologists should be aware that the spread of inaccurate information on skin cancer treatment currently exists on popular social media platforms and may lead to detrimental consequences for patients interested in pursuing alternative or homeopathic approaches.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".