Assessment of the Quality, Understandability, and Reliability of YouTube Videos as a Source of Information on Basal Cell Carcinoma: Web-Based Analysis
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Bibliographic record
Abstract
BACKGROUND: Patients with skin cancer increasingly watch online videos to acquire disease-related information. Until now, no scientific evaluation of the quality of videos available for German-speaking patients with basal cell carcinoma (BCC) has been performed. OBJECTIVE: In this study, we aimed to identify and evaluate videos about BCC provided on YouTube. METHODS: A video search on YouTube was conducted in July 2020, using German BCC-related keywords (eg, "Basalzellkarzinom," "Basaliom," "weißer hautkrebs," and "heller hautkrebs"). The first three pages (ie, 60 videos) were searched by two independent researchers for each keyword. Two authors evaluated videos that met the predefined eligibility criteria. The quality of the information of the videos was evaluated using the DISCERN tool and the Global Quality Scale (GQS). The understandability and actionability were assessed with the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V). The reliability was assessed with the JAMA (Journal of the American Medical Association) criteria score. Subgroup differences were identified using the Kruskal-Wallis test. RESULTS: A total of 41 videos were included in the evaluation. The mean assessment scores were as follows: DISCERN, 3.3 (SD 0.80); GQS, 3.8 (SD 1.1); JAMA, 27.74% (SD 22.1%); understandability, 70.8% (SD 13.3%); and actionability, 45.9% (SD 43.7%). These values indicated that the videos were of medium to good quality and had good understandability, low actionability, and poor reliability. The quality of videos provided by health professionals was significantly higher than that of videos provided by laypersons. CONCLUSIONS: Optimization of health-related videos about BCC is desirable. In particular, adaptation to reliability criteria is necessary to support patient education and increase transparency.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it