A Review and Expert Opinion on the Neuropsychiatric Assessment of Motor Functional Neurological Disorders
Bibliographic record
Abstract
Functional neurological (conversion) disorder (FND) is a prevalent and disabling condition at the intersection of neurology and psychiatry. Advances have been made in elucidating an emerging pathophysiology for motor FND, as well as in identifying evidenced-based physiotherapy and psychotherapy treatments. Despite these gains, important elements of the initial neuropsychiatric assessment of functional movement disorders (FND-movt) and functional limb weakness/paresis (FND-par) have yet to be established. This is an important gap from both diagnostic and treatment planning perspectives. In this article, the authors performed a narrative review to characterize clinically relevant variables across FND-movt and FND-par cohorts, including time course and symptom evolution, precipitating factors, medical and family histories, psychiatric comorbidities, psychosocial factors, physical examination signs, and adjunctive diagnostic tests. Thereafter, the authors propose a preliminary set of clinical content that should be assessed during early-phase patient encounters, in addition to identifying physical signs informing diagnosis and potential use of adjunctive tests for challenging cases. Although clinical history should not be used to make a FND diagnosis, characteristics such as acute onset, precipitating events (e.g., injury and surgery), and a waxing and waning course (including spontaneous remissions) are commonly reported. Active psychiatric symptoms (e.g., depression and anxiety) and ongoing psychosocial stressors also warrant evaluation. Positive physical examination signs (e.g., Hoover's sign and tremor entrainment) are key findings, as one of the DSM-5 diagnostic criteria. The neuropsychiatric assessment proposed emphasizes diagnosing FND by using "rule-in" physical signs while also considering psychiatric and psychosocial factors to aid in the development of a patient-centered treatment plan.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".