German-Language Websites Containing Information About Rare Diseases Lack Quality Indicators
Bibliographic record
Abstract
A Review of: Pauer, F., Litzkendorf, S., Göbel, J., Storf, H., Zeidler, J., & Graf von der Schulenburg, J.-M. (2017). Rare diseases on the Internet: An assessment of the quality of online information. Journal of Medical Internet Research, 19(1), e23. https://doi.org/10.2196/jmir.7056 Abstract Objective – To evaluate the quality of the information contained in websites about rare diseases and to determine if quality varies based on the supplier category of the website. Design – Questionnaire and content analysis. Setting – Germany Subjects – 693 German-language websites Methods – Websites were identified through a Google search: All 8,000 rare diseases (as listed on Orphanet) and their synonyms were entered into Google; the first 20 results for each disease were scanned for sites written in German. A questionnaire designed to measure the quality of information found on the websites was mailed to each identified website provider. For those who did not respond, the survey was completed by the authors using information from the site. A t test was used to examine differences in the quality of information among the types of information providers. Main Results – A total of 693 information suppliers were identified. The suppliers completed 17.7% of the surveys; the other 82.3% were completed by the authors. The majority of information providers were patient organizations/support groups (38.8%) followed by medical institutions (26.8%). Information provided by individuals had the lowest quality rating. There were no statistically significant differences between the quality of information supplied by patient support groups and medical institutions. The highest quality rating was provided by associations/sponsoring bodies. Conclusion – There is not much information available on the Internet regarding rare diseases. Patient support groups and organizations are the largest provider of information. The overall quality rating of information on rare disease websites was found to be low, particularly in areas of accessibility. Website providers should be made aware of how to produce websites of higher quality with greater accessibility.
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 | 0.020 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".