Early Onset of Chronic Rheumatic Disease May Lead to Creative Expression: The Stories of Antoni Gaudí and Maud Lewis
Bibliographic record
Abstract
Many famous artists had rheumatic diseases. Among them, the popular French impressionist painter Pierre-Auguste Renoir, the Russian expressionist painter Alexej von Jawlensky, and the French fauvist and “painter of the light” Raoul Dufy were diagnosed with rheumatoid arthritis (RA), and Paul Klee, a highly renowned Swiss-German painter, with systemic sclerosis. Their medical histories have been widely discussed, and they are examples of how creative art may help in coping with the disease1,2,3,4,5,6. Autoimmune diseases such as RA seem to occur with a higher frequency in artists, and exposure to toxins, such as those found in painting materials, and smoking have been discussed as contributing factors7,8,9. Would a chronic inflammatory pediatric rheumatic disease possibly influence artistic work? Juvenile idiopathic arthritis (JIA) and other chronic rheumatologic conditions with childhood onset have a significant effect on health-related quality of life and physical function, imposing a societal and psychological burden, especially in the prebiologic era10. Art therapy has been shown to be beneficial in adults and children with chronic diseases by reducing stress and pain and enabling the expression of the cognitive and emotional ideas of the patients11,12,13,14,15. To our knowledge and in contrast to the many artists with adult-onset autoimmune rheumatic conditions, there are only 2 famous artists with known chronic pediatric rheumatic diseases: Antoni Gaudi, the unique Catalan architect, and Maud Lewis, one of Canada’s best known and most loved folk art painters. The medical literature discussing Gaudi’s articular symptoms is scarce and we are not aware of any publication on Lewis’s medical history16,17,18. Dufy experienced an episode of self-limited arthritis in childhood, but he remained …
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.014 |
| 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".