Off-label use of atypical antipsychotics in personality disorders
Bibliographic record
Abstract
INTRODUCTION: Personality disorders are among the most persistent and challenging disorders to treat within psychiatry. There is emerging evidence that some personality disorders, particularly borderline personality disorder and, to a lesser extent, schizotypal personality disorder, may benefit from treatment with atypical antipsychotics as well as mood stabilizers. This review examines the evidence for atypical antipsychotics for personality disorders and discusses strengths and limitations of this approach. AREAS COVERED: Searches of Medline and PsycInfo yielded 57 articles related to use of atypical antipsychotics for treatment of personality disorders. Most were relatively small randomized, controlled trials examining atypical antipsychotics for borderline personality disorder; however, the search also yielded two Cochrane reviews examining pharmacotherapy for borderline personality disorder and antisocial personality disorder as well as three other meta-analyses. EXPERT OPINION: There is some evidence that atypical antipsychotics are effective for treating symptom domains in personality disorders, in particular psychotic-like symptoms, impulsivity, aggression and anger. There is no evidence that they improve overall illness severity. Given the high rate of comorbidity between personality disorders and axis I disorders, atypical antipsychotics are best used when these symptom domains are prominent and there is a comorbid axis I condition for which an atypical antipsychotic is indicated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".