Functional Movement Disorder; Importance of Proper Diagnosis and Treatment: A Case Report
Bibliographic record
Abstract
Background: Dystonia is a common movement disorder with a wide range of aetiologies. Delays in the identification and initiation of effective treatments should be minimized to improve patient pain and optimize outcomes. This case report aims to underscore the successful treatment of chronic dystonia with the use of mood-modifying serotonin and norepinephrine reuptake inhibitors (SNRI), and encourage clinicians to consider a diagnosis of functional (psychogenic) movement disorder in patients with dystonia that is refractory to usual treatment. Case Report: This case report describes a 40-year-old woman who presented to a chronic pain clinic for pain related to cervical dystonia with associated head tremor. Her symptoms were refractory to nearly a decade of quarterly botulinum toxin injections. Based on careful evaluation of the patient’s history, a normal neurological examination, increased Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Injustice Experiences Questionnaire (IEQ) scores, and unsuccessful symptom management with botulinum toxin A, a diagnosis of functional movement disorder (FMD) was made. Low-dose Cymbalta was initiated. The patient achieved near complete symptom remission and resolution of her chronic pain within 2 months and achieved near complete resolution in 2 years. Conclusion: A diagnosis of FMD should be considered in all patients with dystonia, but especially in patients who respond inadequately to botulinum toxin injections or other rehabilitation therapies. The treatment of comorbid psychiatric conditions can result in substantial benefits and remission from dystonia due to FMD.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".