Highlighting the Psychiatric Burden: Depression and Anxiety Drive Poor Quality of Life in Focal Dystonia
Bibliographic record
Abstract
INTRODUCTION: Primary focal dystonias are movement disorders linked to dysfunctions in the basal ganglia, which also play a role in neuropsychiatric conditions. While motor symptoms in dystonia are well-documented, the neuropsychiatric comorbidities associated with these disorders, particularly depression, anxiety, obsessive-compulsive disorder (OCD), psychosis and pain, have been little explored. This study aims to assess the prevalence of these psychiatric disorders and their impact on the quality of life (QoL) in patients with cervical dystonia (CD) and oromandibular dystonia (OMD). METHODOLOGY: A total of 140 patients with primary CD and OMD, along with 71 age- and gender-matched healthy controls, were recruited for this study. Participants underwent clinical assessments using the Fahn-Marsden Dystonia Rating Scale, Toronto Western Spasmodic Torticollis Rating Scale and Oromandibular Dystonia Questionnaire for dystonia severity. Psychiatric comorbidities were screened using the Mini International Neuropsychiatric Interview and assessed with rating scales for depression, anxiety, OCD and psychosis. QoL was evaluated using the World Health Organization Quality Of Life - BREF (WHOQOL-BREF). RESULTS: The results showed significantly higher rates of depression (37.9%) and anxiety (29.3%) in patients with dystonia compared to healthy controls (9.9% and 7%, respectively). Both mood disorders were correlated with the severity of dystonia and self-reported pain. The QoL in dystonia patients was significantly worse in the physical and psychological domains. CONCLUSION: Psychiatric comorbidities, particularly depression and anxiety and pain, are highly prevalent in patients with primary CD and OMD, significantly impacting their QoL.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".