Surfing Behind a Boat: Quality and Reliability of Online Resources on Scaphoid Fractures
Bibliographic record
Abstract
BACKGROUND: Patients seeking information and advice on treatment of scaphoid fractures unknowingly confront longstanding medical controversies surrounding the management of this condition. However, there are no studies specifically looking into the quality and reliability of online information on scaphoid fractures. METHODS: We identified 44 unique websites for evaluation using the term "scaphoid fractures". The websites were categorized by type and assessed using the DISCERN score, the Journal of the American Medical Association (JAMA) benchmark criteria and the Health on the net (HON) code. RESULTS: The majority of websites were commercial (n = 13) followed by academic (n = 12). Only seven of the websites were HON certified. The mean DISCERN score was 43.8. Only 4 websites scored 63 or above representing excellent quality with minimal shortcomings but 13 websites scored 38 or below representing poor or very poor quality. The mean JAMA benchmark criteria score was 2.2. The Governmental and Non-Profit Organizations category websites had the highest mean JAMA benchmark score. The websites that displayed the HON-code seal had higher mean DISCERN scores and higher mean JAMA benchmark scores compared to websites that did not display the seal. CONCLUSIONS: Good quality health information is certainly available on the Internet. However, it is not possible to predict with certainty which sites are of higher quality. We suggest clinicians should have a responsibility to educate their patients regarding the unregulated nature of medical information on the internet and proactively provide patients with educational resources and thus help them make smart and informed decisions.
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 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 | 0.020 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".