Readability and Quality of Online Information on Osteoarthritis: An Objective Analysis With Historic Comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is the most common cause of disability in people older than 65 years. Readability of online OA information has never been assessed. A 2003 study found the quality of online OA information to be poor. OBJECTIVE: The aim of this study was to review the readability and quality of current online information regarding OA. METHODS: The term osteoarthritis was searched across the three most popular English language search engines. The first 25 pages from each search engine were analyzed. Duplicate pages, websites featuring paid advertisements, inaccessible pages (behind a pay wall, not available for geographical reasons), and nontext pages were excluded. Readability was measured using Flesch Reading Ease Score, Flesch-Kincaid Grade Level, and Gunning-Fog Index. Website quality was scored using the Journal of the American Medical Association (JAMA) benchmark criteria and the DISCERN criteria. Presence or absence of the Health On the Net Foundation Code of Conduct (HONcode) certification, age of content, content producer, and author characteristics were noted. RESULTS: A total of 37 unique websites were found suitable for analysis. Readability varied by assessment tool from 8th to 12th grade level. This compares with the recommended 7th to 8th grade level. Of the 37, 1 (2.7%) website met all 4 JAMA criteria. Mean DISCERN quality of information for OA websites was "fair," compared with the "poor" grading of a 2003 study. HONcode-endorsed websites (43%, 16/37) were of a statistically significant higher quality. CONCLUSIONS: Readability of online health information for OA was either equal to or more difficult than the recommended level.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it