The Assessment and Treatment of Antipsychotic-Induced Akathisia
Bibliographic record
Abstract
Background: Akathisia is a common and distressing neuropsychiatric syndrome associated with antipsychotic medication, characterised by subjective and objective psychomotor restlessness. The goal of this guideline is to provide clinicians with recommendations on the assessment and treatment of akathisia. Methods: We performed a systematic review of therapeutic studies assessing the treatment of antipsychotic-induced extrapyramidal symptoms. Forty studies on akathisia and 4 systematic reviews evaluating the adverse effects of antipsychotics were used in the formulation of recommendations. Studies were rated for methodological quality using the American Academy of Neurology Risk of Bias Classification system. The overall level of evidence classifications and grades of recommendation were made using the Scottish Intercollegiate Guidelines Network framework. Results: As a good practice point, clinicians should systematically assess akathisia with a validated scale before starting antipsychotics and during antipsychotic dosage titration. For the management of akathisia, there was adequate evidence to allow recommendations regarding antipsychotic dose reduction, antipsychotic polypharmacy, switching antipsychotic medication, and the use of adjuvant medications including beta-blockers, anticholinergics, 5HT 2A antagonists, benzodiazepines, and vitamin B6. Conclusion: The treatment of antipsychotic-induced akathisia should be personalised, with consideration of antipsychotic dose reduction, cessation of antipsychotic polypharmacy, and switching to an antipsychotic with a perceived lower liability for akathisia, before the use of adjuvant medications. The choice of adjuvant medications should favour the more established treatments, with careful consideration of contraindications and side effects. Limitations in the evidence should be acknowledged and prompt cautious prescribing, particularly with respect to the duration of use of adjuvant medications, is warranted.
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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 | 0.027 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".