Tardive Dyskinesia in the Era of Typical and Atypical Antipsychotics. Part 2: Incidence and Management Strategies in Patients with Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Tardive dyskinesia (TD), the principal adverse effect of long-term conventional antipsychotic treatment, can be debilitating and, in many cases, persistent. We sought to explore the incidence and management of TD in the era of atypical antipsychotics because it remains an important iatrogenic adverse effect. METHODS: We conducted a review of TD incidence and management literature from January 1, 1965, to January 31, 2004, using the terms tardive dyskinesia, management, therapy, neuroleptics, antipsychotics, clozapine, olanzapine, risperidone, quetiapine, ziprasidone, and aripiprazole. Additional articles were obtained by searching the bibliographies of relevant references. We considered articles that contributed to the current understanding of both the incidence of TD with atypical antipsychotics and management strategies for TD. RESULTS: The incidence of TD is significantly lower with atypical, compared with typical, antipsychotics, but cases of de novo TD have been identified. Evidence suggests that atypical antipsychotic therapy ameliorates long-standing TD. This paper outlines management strategies for TD in patients with schizophrenia. CONCLUSION: The literature supports the recommendation that atypical antipsychotics should be the first antipsychotics used in patients who have experienced TD as a result of treatment with conventional antipsychotic agents. The other management strategies discussed may prove useful in certain patients.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".