Factors Influencing Triage to Rehabilitation in Functional Movement Disorder
Bibliographic record
Abstract
BACKGROUND: Treatment of functional movement disorder (FMD) should be individualized, yet factors determining rehabilitation engagement have not been evaluated. Subspecialty FMD clinics are uniquely poised to explore factors influencing treatment suitability and triage. OBJECTIVES: To describe our approach and explore factors associated with triage to FMD rehabilitation. METHODS: We conducted a retrospective chart review of 158 consecutive patients with FMD seen for integrated assessment by movement disorders neurology and psychiatry, with the purpose of triage to rehabilitation. Demographic and clinical variables were compared between patients triaged to therapy versus no therapy, and logistic regression was used to explore factors predictive of triage outcome. Change in primary outcome scores were analyzed. RESULTS: Sixty-six patients (42%) were triaged to FMD therapy from July 2019 to December 2021. Patients triaged to therapy were more likely to have a constant movement disorder, gait disorder and/or tremor, hyperarousal, readiness for change, and people pleasing traits. Patients triaged to no therapy demonstrated persistent diagnostic disagreement, an inability to appreciate motor symptom inconsistency, low self-agency, a propensity to dissociate, and cluster B traits. 90% of patients triaged to rehabilitation had improved outcomes. CONCLUSIONS: The ability to "opt-in" to FMD rehabilitation relies on different factors than those relevant to establishing a diagnosis. Unlike many other neurological disorders, a triage and treatment planning step is recommended to identify those likely to meaningfully engage at that time. Holistic assessment through a transdisciplinary lens, and working collaboratively with the patient is essential to prioritize symptoms, determine engagement, and identify treatment targets.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".