Isolated Cervical Dystonia: Diagnosis and Classification
Bibliographic record
Abstract
This document presents a consensus on the diagnosis and classification of isolated cervical dystonia (iCD) with a review of proposed terminology. The International Parkinson and Movement Disorder Society Dystonia Study Group convened a panel of experts to review the main clinical and diagnostic issues related to iCD and to arrive at a consensus on diagnostic criteria and classification. These criteria are intended for use in clinical research, but also may be used to guide clinical practice. The benchmark is expert clinical observation and evaluation. The criteria aim to systematize the use of terminology as well as the diagnostic process, to make it reproducible across centers and applicable by expert and non-expert clinicians. Although motor abnormalities remain central, increasing recognition has been given to nonmotor manifestations, which are incorporated into the current criteria. Three iCD presentations are described in some detail: idiopathic (focal or segmental) iCD, genetic iCD, and acquired iCD. The relationship between iCD and isolated head tremor is also reviewed. Recognition of idiopathic iCD has two levels of certainty, definite or probable, supported by specific diagnostic criteria. Although a probable diagnosis is appropriate for clinical practice, a higher diagnostic level may be required for specific research studies. The consensus retains elements proven valuable in previous criteria and omits aspects that are no longer justified, thereby encapsulating diagnosis according to current knowledge. As understanding of iCD expands, these criteria will need continuous revision to accommodate new advances. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".