Hand It to Dr Google: The Quality of Online Information on Ganglion Cysts
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.
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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 | 0.004 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".