Adequacy of Online Patient Information Resources on Gout and Potentially Curative Urate‐Lowering Treatment
Bibliographic record
Abstract
OBJECTIVE: To assess the content and readability of online patient information resources against the current understanding of gout. METHODS: An online survey was undertaken using Google UK, USA, Australia, and Canada. Information was assessed for content and accuracy on 19 key points regarding core content for gout patient information resources. Readability was assessed using the Flesch-Kincaid Reading Ease score. Fifteen randomly selected websites were reviewed by a blinded second observer. RESULTS: A total of 85 websites were selected. More than 50% of the websites provided no information or had inaccuracies regarding the pathogenesis of gout. Most websites contained information on dietary and lifestyle modifications for treating gout and did not emphasize urate-lowering therapy (ULT) and its potential for cure. Over 75% of the websites had no/inaccurate information on the role of ULT or prophylaxis for preventing gout attacks on starting ULT. The majority of websites were difficult to read, with information in 68% of the websites rated at least fairly difficult. CONCLUSION: Only a few web-based patient information resources provide accurate and easy-to-read information on gout. This study will help physicians direct patients to currently reliable resources, but there is a need to improve many web-based patient information resources, which at present act as barriers to care.
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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 | 0.008 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".