Quality of internet-based decision aids for shoulder arthritis: what are patients reading?
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess the source, quality, accuracy, and completeness of Internet-based information for shoulder arthritis. METHODS: A web search was performed using three common Internet search engines and the top 50 sites from each search were analyzed. Information sources were categorized into academic, commercial, non-profit, and physician sites. Information quality was measured using the Health On the Net (HON) Foundation principles, content accuracy by counting factual errors and completeness using a custom template. RESULTS: After removal of duplicates and sites that did not provide an overview of shoulder arthritis, 49 websites remained for analysis. The majority of sites were from commercial (n = 16, 33%) and physician (n = 16, 33%) sources. An additional 12 sites (24%) were from an academic institution and five sites (10%) were from a non-profit organization. Commercial sites had the highest number of errors, with a five-fold likelihood of containing an error compared to an academic site. Non-profit sites had the highest HON scores, with an average of 9.6 points on a 16-point scale. The completeness score was highest for academic sites, with an average score of 19.2 ± 6.7 (maximum score of 49 points); other information sources had lower scores (commercial, 15.2 ± 2.9; non-profit, 18.7 ± 6.8; physician, 16.6 ± 6.3). CONCLUSIONS: Patient information on the Internet regarding shoulder arthritis is of mixed accuracy, quality, and completeness. Surgeons should actively direct patients to higher-quality Internet sources.
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 | 0.006 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".