Not Every Picture Tells a Story: A Content Analysis of Visual Images in Patient Educational Resources About Gout
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate which concepts about gout and its treatment are reflected in images in online educational resources about gout. METHODS: A Google search was performed to identify English-language patient resources from medical and health organizations and health education websites in 7 countries: Australia, Canada, Ireland, New Zealand, South Africa, UK, and USA. Two raters independently coded the images in the resources into 5 main categories: clinical presentations of gout, urate/monosodium urate (MSU) crystals, medicines, food/healthy lifestyle, and other advice for people with gout. RESULTS: In total, 103 resources were identified; 28 resources without images were excluded. Seventy-one educational resources with a total of 310 images were included in the study sample. Of the 310 images, clinical presentations of gout were depicted in 92 images (30%), food/healthy lifestyle in 73 images (24%), urate/MSU crystals in 50 (16%), medicines in 14 (5%). Urate-lowering medication was shown only in 1 image (0.3%) and just 6 images (2%) depicted a serum urate target. Ninety-one images (29%) did not convey specific information about gout. CONCLUSION: Key concepts about gout and treatment are underrepresented in the images used in educational resources for patients. A large proportion of the images do not convey useful information about gout or its management.
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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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".