Non-motor symptoms in dystonia: from diagnosis to treatment
Bibliographic record
Abstract
The Dystonia Medical Research Foundation organized an expert virtual workshop in March 2023 to review the evidence on non-motor symptoms across the spectrum of dystonia, discuss existing assessment methods, need for their harmonisation and roadmap to achieve this, and evaluate potential treatment approaches. Albeit the most investigated non-motor domains, experts highlighted the need to identify the most accurate screening procedure for depression and anxiety, clarify their mechanistic origin and quantify their response to already available therapies. Future exploration of sleep disruption in dystonia should include determining the accuracy and feasibility of wearable devices, understanding the contribution of psychotropic medication to its occurrence, and defining the interaction between maladaptive plasticity and abnormal sleep patterns. Despite recent advances in the assessment of pain in dystonia, more research is needed to elucidate the relative importance of different mechanisms called into play to explain this impactful sensory feature and the most appropriate treatments. Amongst the different non-motor features investigated in dystonia, cognitive dysfunction and fatigue require an in-depth observation to evaluate their functional impact, their clinical profile and assessment methods and, in the case of cognition, whether impairment represents a prodrome of dementia. Finally, experts identified the development and field validation of a self-rated screening tool encompassing the full spectrum of non-motor symptoms as the most urgent step towards incorporating the management of these features into routine clinical practice.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".