Combined Therapy of Major Depression with Concomitant Borderline Personality Disorder: Comparison of Interpersonal and Cognitive Psychotherapy
Bibliographic record
Abstract
OBJECTIVE: The combination of antidepressants and brief psychotherapies has been proven more efficacious in treating major depression and is particularly recommended in patients with concomitant personality disorders. We compare the effects of 2 combined therapies, fluoxetine and interpersonal therapy (IPT) or fluoxetine and cognitive therapy (CT), on major depression in patients with borderline personality disorder (BPD). METHOD: Thirty-five consecutive outpatients with a diagnosis of BPD and a major depressive episode (not bipolar and not psychotic) were enrolled. They were randomly assigned to 1 of the 2 combined treatments and treated for 24 weeks. Assessment included a semistructured interview, Clinical Global Impression (CGI) scale, Hamilton Depression Rating Scale (HDRS), Hamilton Anxiety Rating Scale (HARS), Beck Depression Inventory-II (BDI-II), Social and Occupational Functioning Assessment Scale (SOFAS), Satisfaction Profile (SAT-P) for quality of life (QOL), and Inventory of Interpersonal Problems (IIP-64). Statistical analysis was performed using the univariate General Linear Model to calculate the effects of duration and type of treatment. RESULTS: No significant differences between treatments were found at CGI, HDRS, BDI-II, and SOFAS score. Combined treatment with CT had greater effects on HARS score and on psychological functioning factor of SAT-P. Combined treatment with IPT was more effective on social functioning factor of SAT-P and on domains domineering or controlling and intrusive or needy of IIP-64. CONCLUSIONS: Both combined therapies are efficacious in treating major depression in patients with BPD. Differences between CT and IPT concern specific features of subjective QOL and interpersonal problems. These findings lack reliable comparisons and need to be replicated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".