Hand It to Dr Google: The Quality of Online Information on Ganglion Cysts
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: The internet is becoming a common source of health information for hand surgery patients. This study evaluates the quality of web-based resources on ganglion cysts of the hand. Methods: We completed a search for “ganglion cyst” on 3 search engines (Google, Dogpile, and Yippy). The quality of the top-100 patient education websites was assessed using a validated internet rating tool. Websites were evaluated based on affiliation, accountability, currency, interactivity, website organization, readability, coverage, and accuracy. Results: Of the 100 websites, the majority (74%) had commercial affiliations. Only 34% of websites identified an author, and even fewer identified the authors’ credentials (27%) or affiliations (26%). A third of the websites cited references, and less than half provided an update date. The average readability based on Flesch-Kincaid grade level was 9.2, and only 3% could be read at or below 6th grade reading level. Prevention was the most poorly covered topic at 13% due to omission. In all, 66% of the websites were completely accurate in terms of global accuracy. Websites were most likely to present inaccurate information on treatment, often failing to mention conservative treatment (watch-and-wait approach) or promoting the use of natural health products. We also found 5% of websites presented closed rupture of the ganglion cyst as a legitimate home remedy. Conclusions: The overall quality of online information on ganglion cysts is highly variable and may occasionally be harmful for patients. It is increasingly important for physicians to prompt patients about their internet use.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it