Reviews Chorea: A Journey through History
Bibliographic record
Abstract
The original descriptions of chorea date from the Middle Ages, when an epidemic of ‘‘dancing mania’ ’ swept throughout Europe. The condition was initially considered a curse sent by a saint, but was named ‘‘Saint Vitus’s dance’ ’ because afflicted individuals were cured if they touched churches storing Saint Vitus’s relics. Paracelsus coined the term chorea Sancti Viti and recognized different forms of chorea (imaginativa, lasciva, and naturalis). In the 17th century, Thomas Sydenham provided an accurate description of what he termed chorea minor. He also described rheumatic fever but did not associate it with chorea. It was only in 1850 that See established a relationship between chorea and rheumatic disease. A connection with cardiac involvement was soon recognized and in 1866 Roger postulated that chorea, arthritis, and heart disease had a common cause. The last quarter of the 19th century is marked by the works of Jean-Martin Charcot, Silas Weir Mitchell, William Osler, and William Richard Gowers, all of paramount importance in the refinement of the definition of chorea, its causes, and differential diagnosis. In 1841, Charles Oscar Waters gave a concise account of a syndrome, likely to be Huntington’s disease (HD), later described further by George Huntington and named after him. In 1955, the Venezuelan physician Americo Negrette published a book describing communities in the State of Zulia in Venezuela, with unusual numbers of individuals with chorea. Negrette’s works culminated in the creation of the Venezuela project and the subsequent discovery of
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".