MétaCan
Menu
Back to cohort
Record W7099832591

Reviews Chorea: A Journey through History

2016· article· en· W7099832591 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsChoreaCurseGeorge (robot)Quarter (Canadian coin)HonorSAINT
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.437
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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.

Opus teacher head0.110
GPT teacher head0.261
Teacher spread0.151 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicLaw, AI, and Intellectual PropertyFrench-language works237,207