Content and Quality of Websites for Patients With Chronic Kidney Disease: An Environmental Scan
Bibliographic record
Abstract
Background: Although numerous websites for patients with chronic kidney disease (CKD) are available, little is known about their content and quality. Objective: To evaluate the quality of CKD websites, and the degree to which they align with information needs identified by patients with CKD. Methods: We identified websites by entering “chronic kidney disease” in 3 search engines: Google.com (with regional variants for Australia, Canada, the United Kingdom, and the United States), Bing.com, and Yahoo.com. We included the first 50 unique English-language sites from each search. We evaluated website content using a 30-point scale comprising 8 priority content domains identified by patients with CKD ( understanding CKD, diet, symptoms, medications, mental/physical health, finances, travel, and work/school). We used standardized tools to evaluate usability, reliability, and readability (DISCERN, HONcode, LIDA, Reading Ease, and Reading Grade Level). Two reviewers independently conducted the search, screen, and evaluation. Results: Of the 2093 websites identified, 115 were included. Overall, sites covered a mean (SD) of 29% (17.8) of the CKD content areas. The proportion of sites covering content related to understanding CKD, symptoms, and diet was highest (97%, 80%, and 72%, respectively). The proportion of sites covering travel, finances, and work/school content was lowest (22%, 12%, and 12%, respectively). The mean (SD) scores for DISCERN, LIDA and HONcode were 68% (14.6), 71% (14.4), and 75% (17.2), respectively, considered above average for usability and reliability. The mean (SD) Reading Grade Level was 10.6 (2.8) and Reading Ease was 49.8 (14.4), suggesting poor readability. Conclusions: Although many CKD web sites were of reasonable quality, their readability was poor. Furthermore, most sites covered less than 30% of the content patients identified as important for CKD self-management. These results will inform content gaps in internet-accessible information on CKD self-management that should be addressed by future eHealth web-based tools.
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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".