Is the internet a sufficient source of information on sarcoidosis?
Bibliographic record
Abstract
Introduction Many patients use the internet as a source of health information. Sarcoidosis is a complex disease, and internet resources have not yet been analyzed for reliability and content on sarcoidosis. Aims Our study aimed to investigate the content and the quality of information on sarcoidosis provided by internet resources. Methods Google, Yahoo, and Bing were searched for the term “sarcoidosis,” and the first 200 hits were saved in each case. Those websites that met the inclusion criteria (English language, no registration fees, and relevant to sarcoidosis) were then analyzed by two independent investigators for readability, quality (HON, JAMA, and DISCERN), and content (25 predefined key facts) of the provided information. Results The websites were most commonly scientific or governmental (n = 57, 46%), and the median time since the last update was 24 months. Quality was rated with a median JAMA score of 2 (1; 4) and a median overall DISCERN score of 2.4 (1.1; 4.1), both scores represent partially sufficient information. In total, 15% of websites had a HON certificate. Website content measured by the median key fact score was 19 (ranging from 2.5 to 25) with the lowest scores for acute vs. chronic course of the disease, screening for extrapulmonary disease, and diffuse body pain. Poor results were achieved in industry websites and blogs (p = 0.047) with significant differences regarding definition (p = 0.004) and evaluation (p = 0.021). Discussion Sarcoidosis-related content of internet resources is partially sufficient; however, several important aspects are frequently not addressed, and the quality of information is moderate. Future directions should focus on providing reliable and comprehensive information on sarcoidosis; physicians from different disciplines and patients including self-support groups should collaborate on achieving this.
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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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".