Historical aspects of studying craniocervical dystonia
Bibliographic record
Abstract
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 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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".