Clozapine-Induced Seizures: Recognition and Treatment
Bibliographic record
Abstract
OBJECTIVES: To inform clinicians about the types of seizures that can be induced by clozapine and to provide recommendations for treatment. METHODS: We identified articles on clozapine-induced seizures from a MEDLINE search of the English-language literature from 1978 to July 2006. The frequency of each type of seizure and the dosages of clozapine associated with seizures were compiled. In addition to this review, we report a new case illustrating the challenge of diagnosing subtle seizure activity. RESULTS: The tonic-clonic variety is the most frequently described clozapine-induced seizure. Myoclonic and atonic seizures together constitute about one-quarter of the reported seizures. The mean dosage of clozapine associated with seizures is not high (less than 600 mg daily). CONCLUSIONS: It may be difficult for clinicians to recognize subtle types of clozapine-induced seizures, such as myoclonic, atonic, or partial seizures. Clinicians should not place excessive reliance on the plasma level of clozapine or electroencephalogram findings to predict the occurrence of seizures. When a first seizure occurs, it is recommended that the dosage of clozapine be reduced or an alternative antipsychotic agent be employed. If a second seizure occurs, an anticonvulsant drug should be started. Special attention should be paid when commencing or discontinuing concurrent medication that may affect the plasma level of clozapine.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".