YouTube-videos for patient education in lymphangioleiomyomatosis?
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: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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