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Record W3214772330 · doi:10.25692/acen.2019.4.12

Historical aspects of studying craniocervical dystonia

2019· article· en· W3214772330 on OpenAlexaff
Zifa G. Khayatova, Zuleykha Zalyalova

Bibliographic record

VenueAnnals of Clinical and Experimental Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsDystoniaPhysical medicine and rehabilitationMedicinePsychologyNeuroscience

Abstract

fetched live from OpenAlex

The study of dystonic hyperkinesias has a thousand-year-old history. Beginning with drawings and sculptures from antiquity and up to the present day, modern ideas gradually developed about the phenomenology, origin, and treatment methods of dystonia. Mentions of spastic torticollis, blepharospasm and Meige syndrome can even be found in the writings of Hippocrates and Celsus. Images and monuments from antiquity and ancient civilizations indicate the existence of focal dystonias in those times. The Middle Ages left science with records of cervical dystonia and numerous illustrations in religious images. The first well-known mention of the term ‘torticollis’ belongs to François Rabelais. The term started to appear in medical texts later on. One of the earliest medical records on cervical dystonia was made by the Swiss physician Felix Platerus. During the Age of Enlightenment, dystonias became a separate class in disease classification. Modern tendencies in studying dystonia are characterized by identifying the genes responsible for different forms of primary dystonia along with description of their phenotypes. There is an ongoing research on the role of mental disorders in the clinical presentation of dystonia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.116
GPT teacher head0.401
Teacher spread0.285 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2019
Admission routes1
Has abstractyes

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