Effects of substance use disorder on treatment process and outcome in a ten-session psychiatric treatment for borderline personality disorder
Bibliographic record
Abstract
BACKGROUND: Dual diagnosis is common in Borderline Personality Disorder (BPD), one of the most common being Substance Use Disorder (SUD). Previous studies have shown that general psychiatric management (GPM) was effective in reducing borderline symptoms. In the present study, we tested whether the short GPM was as effective in the BPD + SUD as in the BPD group. METHODS: We analysed a group of 99 patients presenting a BPD. 51 of these patients presented a SUD. The BPD group and the BPD + SUD group received a manual-based short variant of the GPM treatment. Previous studies have shown that a 10-session version of GPM was effective in reducing borderline symptoms at the end of the treatment (Psychother Psychosom 83:176-86, 2014). RESULTS: We found no significant difference in the reduction of general symptoms, which diminished in both groups. The specific borderline symptoms were also reduced in both groups, but there was a slightly higher reduction of the borderline symptoms in the SUD group. The therapeutic alliance progressed positively in all groups. Moreover, the alliance increased more over time in the SUD group. CONCLUSION: The short variant of GPM seems to be effective in BPD treatment independently from the presence of SUD. Therefore, this treatment could be an effective entry-level treatment for patients with dual diagnosis as well as patients with BPD only. Further studies are needed to confirm efficacy and long-term outcome. TRIAL REGISTRATION: The trial was registered at ClinicalTrial.gov (identifier NCT01896024 ).
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".