Clozapine and tardive dyskinesia in patients with schizophrenia: A systematic review
Bibliographic record
Abstract
BACKGROUND: It is commonly recommended that a switch to clozapine be implemented in the face of tardive dyskinesia, even if current treatment involves another "atypical" agent. However, reports do indicate clozapine carries a liability for tardive dyskinesia. AIMS: This review sought to evaluate clozapine in relation to tardive dyskinesia in the context of available evidence. METHODS: . References from major review articles were searched for additional relevant publications. Studies were included if they investigated: tardive dyskinesia in clozapine-treated patients diagnosed with schizophrenia spectrum disorders, and reported on two or more assessments of tardive dyskinesia severity measured by the Abnormal Involuntary Movement Scale; or clozapine's tardive dyskinesia liability. RESULTS: 905 clozapine-treated participants); however, the minimum required dose and effect of withdrawal requires further investigation. The majority of reports which address clozapine's liability for tardive dyskinesia are case studies (11 of 14 reports, 79%), and clozapine was only the first-line treatment in one of the remaining three studies reporting treatment-emergent dyskinetic symptoms with clozapine in 12% of patients. No significant between-drug differences were identified comparing clozapine's risk to other atypical antipsychotics. CONCLUSIONS: Research to date supports switching to clozapine for the purpose of reducing tardive dyskinesia risk and/or treating existing tardive dyskinesia, but prospective randomized controlled trials are necessary if we are to substantiate existing recommendations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".