The Quality, Content, Accuracy and Readability of Information about Sickle Cell Anemia on the Internet
Bibliographic record
Abstract
Abstract Objective: A comprehensive website review was conducted to assess the quality, content and readability of online information for teens with sickle cell anemia (SCA). Methods. Key words relevant to SCA were searched across the five most commonly used search engine domains. Websites that contained information about the diagnosis and management of SCA were reviewed. Quality of the information was appraised using the validated DISCERN tool. Two physicians rated website content completeness and accuracy independently. Readability of the sites was documented using SMOG scores and the Flesch Reading ease scoring system. Results. Search results yielded more than 600 sites of which 25 websites met the criteria for DISCERN quality review. The majority of sites targeted parents and only 5/25 (20%) were specific to teens with SCA. The overall quality of the website information was "fair", with the average DISCERN quality rating score being 50.1 (± 9.3, range 31.0-67.5). Only 12/25(48%)of the websites had DISCERN scores above 50 (mean 57.37 + 4.93, range 52.17-67.50). The average completeness score of the sites was 20 out of 29 (±5; range 12-27) and accuracy was consistently rated 4/4, indicating high accuracy with moderate completeness. The average SMOG score was 12.44 (±2.01; range 10.21-16.08), and the mean Flesch Reading Ease score was 46.45 (±13.22; range 17.50-66.10) indicating that the material was written well above the acceptable level for patient education materials. Conclusion. Given the paucity of high quality Internet health information at an appropriate reading level for teens with SCA, there is a critical need for the development of Internet programs to meet their unique self-management needs. Disclosures No relevant conflicts of interest to declare.
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 | 0.023 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".