Psychiatric Comorbidities in Functional Movement Disorders: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Functional movement disorders (FMD) are characterized by abnormal movements and motor symptoms incongruent with a known structural neurologic cause. While psychological stressors have long been considered an important risk factor for developing FMD, little is known about the impact of psychiatric comorbidities on disease manifestations or complexity. OBJECTIVES: To compare characteristics of FMD patients with co-occurring mood and trauma-related psychiatric conditions to FMD patients without psychiatric conditions. METHODS: We performed a retrospective cohort study of patients seen in the University of Colorado Health system between January 1, 2015 and December 31, 2019. Patients were included if they had a diagnosis of FMD, determined by ICD-10 coding and ≥1 phenomenology-related diagnostic code (tremor, gait disturbances, ataxia, spasms, and weakness), and at least one encounter with a neurology specialist. Fisher's exact and unpaired t-tests were used to compare demographics, healthcare utilization, and phenomenologies of patients with psychiatric conditions to those with none. RESULTS: = 0.001). Suicidal ideation (8.4%) and self-harm (4.1%) were only observed amongst patients with comorbid psychiatric conditions. CONCLUSIONS: Patients with FMD and comorbid psychiatric conditions require more healthcare resources and have greater disease complexity than patients without psychiatric illness. This may have implications for treatment of patients without comorbid psychiatric conditions who may benefit from targeted physiotherapy alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".