Bibliographic record
Abstract
Background: Peripherally-induced movement disorders (PIMD) should be considered when involuntary or abnormal movements emerge shortly after an injury to a body part. A close topographic and temporal association between peripheral injury and onset of the movement disorders is crucial to diagnosing PIMD. PIMD is under-recognized and often misdiagnosed as functional movement disorder, although both may co-exist. Given the considerable diagnostic, therapeutic, and psychosocial-legal challenges associated with PIMD, it is crucial to update the clinical and scientific information about this important movement disorder. Methods: A comprehensive PubMed search through a broad range of keywords and combinations was performed in February 2023 to identify relevant articles for this narrative review. Results: The spectrum of the phenomenology of PIMD is broad and it encompasses both hyperkinetic and hypokinetic movements. Hemifacial spasm is probably the most common PIMD. Others include dystonia, tremor, parkinsonism, myoclonus, painful leg moving toe syndrome, tics, polyminimyoclonus, and amputation stump dyskinesia. We also highlight conditions such as neuropathic tremor, pseudoathetosis, and MYBPC1-associated myogenic tremor as examples of PIMD. Discussion: There is considerable heterogeneity among PIMD in terms of severity and nature of injury, natural course, association with pain, and response to treatment. As some patients may have co-existing functional movement disorder, neurologists should be able to differentiate the two disorders. While the exact pathophysiology remains elusive, aberrant central sensitization after peripheral stimuli and maladaptive plasticity in the sensorimotor cortex, on a background of genetic (two-hit hypothesis) or other predisposition, seem to play a role in the pathogenesis of PIMD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".