Psychogenic movement disorders: Past developments, current status, and future directions
Bibliographic record
Abstract
As the field of movement disorders has developed and matured over the past 25 years, psychogenic movement disorders have become increasingly recognized in subspecialty clinics. The diagnosis can be challenging and should be based on positive features rather than a purely exclusionary approach. The clinical phenotype can be quite varied, although certain categories of abnormal movement are more common than others. Electrophysiological studies may be particularly useful in establishing the diagnosis, especially with respect to tremor and myoclonus, and an argument can be made for adding a "laboratory-supported definite" category to earlier classification schemes. The diagnosis of psychogenic dystonia remains a major challenge, although there are some recent promising developments with respect to the evaluation of cortical plasticity that require further study. The pathogenesis of psychogenic movement disorders is poorly understood; insights may be provided from the study of other neurological conversion disorders such as psychogenic hemiparesis. Psychogenic movement disorders typically result in considerable disability and negatively impact quality of life to the same or greater extent than do many organic movement disorders. Treatment is extremely challenging, and many patients experience chronic disability despite various therapeutic interventions. Given the personal and societal impact of these problems, further advances in our understanding of their pathogenesis and the subsequent development of effective therapies are sorely needed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".