YouTube-videos for patient education in lymphangioleiomyomatosis?
Bibliographic record
Abstract
BACKGROUND: The Internet is commonly used by patients to acquire health information. To date, no studies have evaluated the quality of information available on YouTube regarding lymphangioleiomyomatosis (LAM). Our aim was to determine the quality and content of YouTube videos regarding LAM and to compare the information provided with current knowledge and guidelines about the disease. METHODS: The first 200 video hits on YouTube in English for the search term "lymphangioleiomyomatosis" were recorded. All videos suitable for patient education on LAM were included. Video quality was analyzed independently by two investigators utilizing the Health on the Net (HONcode) score, which assesses whether websites provide understandable, accessible, and trustworthy health information; the DISCERN score, which evaluates the quality of information about treatment decisions; and a newly developed LAM-related content score (LRCS) with 31 guideline elements. RESULTS: The search identified 64 eligible videos. The "engagement rate" of 0.3 was low, with a median number of views of 408 (range 42-73,943), a median of 4 likes (range 0-2082), and the majority (53%) receiving a low HONcode score (≤ 2) and only 10% of videos achieving a high score (> 5). The median DISCERN score was 28 (range 15-61, maximum possible score 80), indicating poor video quality and reliability. The median LRCS was 8 (range 0-29, maximum possible score 31) and videos frequently failed to provide sources of information. CONCLUSIONS: Online resources could contribute to the limited and often inaccurate information available to patients with LAM, with only a few YouTube videos providing high-quality patient-relevant information.
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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 | 0.002 | 0.001 |
| 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) | 0.028 | 0.003 |
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".