Is YouTube a sufficient source of information on Sarcoidosis?
Bibliographic record
Abstract
Background: The internet is a common source of health information for patients and caregivers. To date, content and information quality of YouTube videos on sarcoidosis has not been studied. Methods: Of the first 200 results under the search term "sarcoidosis," all English-language videos with content directed at patients were included. Two independent investigators assessed the content of the videos based on 25 predefined key features (content score with 0-25 points), as well as reliability and quality (HONCode score with 0-8, DISCERN score with 1-5 points). Misinformation contained in the videos was described qualitatively. Results: The majority of the 85 included videos were from an academic or governmental source (n= 63, 74 %), and median time since upload was 33 months (IQR 10-55). Median video duration was 8 minutes (IQR 3-13) and had a median of 2,044 views (IQR 504-13,203). Quality assessment suggested partially sufficient information: mean HONCode score was 4.4 (SD 0.9) with 91% of videos having a medium quality HONCode evaluation. Mean DISCERN score was 2.3 (SD 0.5). Video content was poor with a mean of 10.5 points (SD 0.6). Absent key features included information on the course of disease (6%), presence of substantial geographical variation (7%), and importance of screening for extrapulmonary manifestations (11%). HONCode scores were higher in videos from academic or governmental sources (p=0.003), particularly regarding “transparency of sponsorship” (p< 0.001). DISCERN and content scores did not differ by video category. Conclusions: Most YouTube videos present incomplete information of poor quality, particularly around missing references. Trustworthy and up to date information is needed.
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.
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 | 0.004 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".