Treatment Recommendations for Tardive Dyskinesia
Bibliographic record
Abstract
BACKGROUND: Tardive dyskinesia is a movement disorder characterised by irregular, stereotyped, and choreiform movements associated with the use of antipsychotic medication. We aim to provide recommendations on the treatment of tardive dyskinesia. METHODS: We performed a systematic review of studies of the treatment of tardive dyskinesia. Studies were rated for methodological quality using the American Academy of Neurology Risk of Bias Classification system. Overall level of evidence classifications and grades of recommendation were made using the Scottish Intercollegiate Guidelines Network framework. RESULTS: Preventing tardive dyskinesia is of primary importance, and clinicians should follow best practice for prescribing antipsychotic medication, including limiting the prescription for specific indications, using the minimum effective dose, and minimising the duration of therapy. The first-line management of tardive dyskinesia is the withdrawal of antipsychotic medication if clinically feasible. Yet, for many patients with serious mental illness, the discontinuation of antipsychotics is not possible due to disease relapse. Switching from a first-generation to a second-generation antipsychotic with a lower D2 affinity, such as clozapine or quetiapine, may be effective in reducing tardive dyskinesia symptoms. The strongest evidence for a suitable co-intervention to treat tardive dyskinesia comes from tests with the new VMAT inhibitors, deutetrabenazine and valbenazine. These medications have not been approved for use in Canada. CONCLUSION: Data on tardive dyskinesia treatment are limited, and the best management strategy remains prevention. More long-term safety and efficacy data are needed for deutetrabenazine and valbenazine, and their routine availability to patients outside of the USA remains in question.
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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 | 0.024 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".