Rehabilitation for Functional Dystonia: Cases and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Functional dystonia (FD) is a common subtype of functional movement disorder. FD can be readily diagnosed based on positive signs and is potentially treatable with rehabilitation. Despite this, clinical outcomes remain variable and a gold standard approach to treatment is lacking. CASES: Here we present four cases of axial and limb functional dystonia who were treated with integrated rehabilitation and improved. The therapy approach and clinical outcomes are described, including videos. LITERATURE REVIEW: A literature review evaluated the published treatment strategies for the treatment of functional dystonia. Out of 338 articles, 25 were eligible for review and included mainly case reports and case series. Most patients received more than one treatment modality. Non-invasive therapies, commonly physiotherapy and psychological approaches were mostly associated with positive outcomes. Multiple treatments commonly used in dystonia were used, including botulinum toxin injections, pharmacotherapy and surgery, leading to variable outcomes. CONCLUSION: Therapy should be personalized to the clinical presentation. In challenging cases, initiation of a multidisciplinary approach may provide benefit regardless of etiology. Pharmacotherapy should be used judiciously, and surgical therapy should be avoided.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".