Integrated model of care for functional movement disorder: targeting brain, mind and body
Bibliographic record
Abstract
Purpose To describe the therapy approaches and clinical outcomes of an integrated care model for patients with functional movement disorder (FMD).Materials and Methods A retrospective chart review was conducted for all treated individuals with a primary diagnosis of FMD between January 2020 and July 2022. Patients received time-limited integrated therapy (n = 21) (i.e., simultaneous therapy delivered by psychiatry, neurology and physiotherapy), physiotherapy (n = 18) or virtual physiotherapy alone (n = 9). Primary outcomes included the Simplified-Functional Movement Disorders Rating Scale (S-FMDRS) and Clinical Global Impression-Improvement scale (CGI-I) collected at baseline and post-intervention.Results Forty-eight patients completed treatment (42% male; mean age, 48.5 ± 16.6 years, median symptom duration 30 months). The most common presentations were gait disorder, tremor and mixed hyperkinetic FMD. Common comorbidities included pain and fatigue. Three-quarters of patients had a comorbid psychiatric diagnosis. There was a significant reduction in S-FMDRS score following therapy (71%, p < 0.0001) and 69% had “much” or “very much” improved on the CGI-I. There was no difference between therapy groups. Attendance rates were high for both in-person (94%) and virtual (97%) visits.Conclusions These findings support that a time-limited integrated model of care is feasible and effective in treating patients with FMD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".