Reduction in Neuroleptic-Induced Movement Disorders After a Switch to Quetiapine in Patients With Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Persistent neuroleptic-induced movement disorders limit effective pharmacological management of psychotic disorders. Although antipsychotic switching is a common strategy for managing extrapyramidal side effects (EPSs), there is insufficient empirical support to guide the clinician. We designed the present study to examine whether patients with preexisting EPS switched to quetiapine would show greater reduction in EPS compared with control patients. METHODS: Twenty-two patients with schizophrenia meeting clinical criteria for tardive dyskinesia or coexisting parkinsonism were randomized either to switch from their current antipsychotic to quetiapine (n = 13) or to remain on their current treatment (n = 9). A battery of standard clinical assessments for EPS along with electromechanical instrumental measures was administered before randomization and again 1 and 3 months postrandomization. RESULTS: We observed significant reduction in parkinsonism (P < 0.001) and akathisia (P = 0.02) based on clinical assessments and dyskinesia (P < 0.05) based on instrumental assessment for the quetiapine group. Subjects remaining on current treatment exhibited an increase in rigidity (P < 0.05) based on instrumental measures. CONCLUSIONS: These findings support the switching to quetiapine in the management of preexisting neuroleptic-induced extrapyramidal side effects.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".