Famous Artists Who Suffer(ed) From Rheumatic Diseases: A Systematic Review
Bibliographic record
Abstract
To the Editor: Rheumatic diseases (RD) occur at a relatively high frequency in the population. We hypothesize that some of these diseases may have affected some artists and possibly influenced their works. In this article, a systematic review of all studies that described the occurrence of rheumatic diseases in famous artists in the world was performed. A PEO format (P = population, E = exposure, O = outcome) to elaborate the research question, “Famous artists (P), with rheumatic diseases (E), have their works changed (O) due to these diseases?” was used. An extensive literature search in Pubmed/MEDLINE, Scielo, and LILACS, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines, was performed without language restriction, from 1965 to June 2020. After the review of titles and abstracts, 116 out of 1026 articles were selected for reading the full texts, of which 68 were selected for this review. We have identified 20 famous artists who had RD. Table 1 is a summary of all data regarding the artists1–20. Most of them had rheumatoid arthritis (RA) as … Address correspondence to Dr. J.F. de Carvalho, Rua das Violetas, 42, ap. 502, Pituba, Salvador, Bahia, Brazil. Email: jotafc{at}gmail.com.
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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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".