Borderline Personality Disorder: Bipolarity, Mood Stabilizers and Atypical Antipsychotics in Treatment
Bibliographic record
Abstract
In this article, it is aimed to review the efficacies of mood stabilizers and atypical antipsychotics, which are used commonly in psychopharmacological treatments of bipolar and borderline personality disorders. In this context, common phenomenology between borderline personality and bipolar disorders and differential features of clinical diagnosis will be reviewed in line with the literature. Both disorders can demonstrate common features in the diagnostic aspect, and can overlap phenomenologically. Concomitance rate of both disorders is quite high. In order to differentiate these two disorders from each other, quality of mood fluctuations, impulsivity types and linear progression of disorders should be carefully considered. There are various studies in mood stabilizer use, like lithium, carbamazepine, oxcarbazepine, sodium valproate and lamotrigine, in the treatment of borderline personality disorder. Moreover, there are also studies, which have revealed efficacies of risperidone, olanzapine and quetiapine as atypical antipsychotics. It is not easy to differentiate borderline personality disorder from the bipolar disorders. An intensively careful evaluation should be performed. This differentiation may be helpful also for the treatment. There are many studies about efficacy of valproate and lamotrigine in treatment of borderline personality disorder. However, findings related to other mood stabilizers are inadequate. Olanzapine and quetiapine are reported to be more effective among atypical antipsychotics. No drug is approved for the treatment of borderline personality disorder by the entitled authorities, yet. Psychotherapeutic approaches have preserved their significant places in treatment of borderline personality disorder. Moreover, symptom based approach is recommended in use of mood stabilizers and atypical antipsychotics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".