A Scoping Review of Methods, Guidelines, and Protocols Used for Dose Reduction or Discontinuation of Antipsychotic Medication in People Affected by Psychotic Disorders
Bibliographic record
Abstract
BACKGROUND: Individuals affected by psychotic disorders often decide to have a trial of dose reduction or discontinuation of their antipsychotic medications; however, there is a lack of evidence-based guidance for this practice. There is limited knowledge on the different methods for dose reduction or discontinuation of antipsychotic medications in which methods might be the safest and most effective. This scoping review aims to identify methods of antipsychotic dose reduction and discontinuation, to develop a comprehensive inventory of tapering strategies. STUDY DESIGN: A systematic search was performed in PubMed, EMBASE, PsycINFO, and Cochrane to identify publications involving antipsychotic dose reduction or discontinuation. We included primary studies, systematic reviews, theoretical papers, and guidelines. STUDY RESULTS: A total of 9005 unique articles were identified, and full text screening was conducted on 459 of these. Thirty-two articles were included in the final analysis: 28 related to oral antipsychotic medications and 4 to long acting injectables. Three broad categories of dose reduction/discontinuation were identified-immediate, rapid (<4 weeks), and gradual (short 4-26 weeks and long≥26 weeks). Further subtypes were identified, with reduction based on either the original starting dose or the most recent dose after the last reduction. The hyperbolic method estimates the dose reduction based on a gradual reduction in the D2 receptor occupancy. CONCLUSIONS: A wide range of antipsychotic tapering methods have been implemented internationally with no consensus on the best approach. The results of this scoping review could help to inform research on establishing the most effective methods of antipsychotic discontinuation.
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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 | 0.161 | 0.320 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.057 | 0.046 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".