Movement disorders in patients taking anticonvulsants
Bibliographic record
Abstract
BACKGROUND: A wide variety of movement disorders may occur as a consequence of the administration of antiepileptic drugs (AEDs). Although it has been suggested that the risk of parkinsonism is 10-fold higher in those taking valproate as compared with other AEDs, there have been no large, systematic trials assessing this. AIM: To establish more precisely the prevalence of and risk factors for developing parkinsonism associated with valproate use,and to assess the occurrence of movement disorders with the newer AEDs. METHODS: Patients with epilepsy were recruited from the Toronto Western Hospital Epilepsy Clinic (University of Toronto, Toronto, Ontario, Canada). Each patient was examined by a movement disorder specialist who was blinded to the treatment status of the patient. RESULTS: 201 patients were included. Postural tremor was the most common movement disorder (45%), followed by parkinsonism (4.5%). The odds of having parkinsonism were 5 times higher with valproate than with other AEDs. No single factor predicted the presence of parkinsonism; however, many (5/9) of the patients concurrently used other drugs or had comorbidities that could have caused or exacerbated parkinsonism. None of the newer AEDs were clearly associated with the presence of movement disorders; however, the numbers were too small to make a formal analysis. CONCLUSION: Although the risk of parkinsonism with valproate is higher than with other AEDs, it is lower than originally reported. The cases available were not enough to accurately comment on the prevalence of movement disorders with the newer AEDs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".