Maladaptive defense mechanisms moderate treatment outcome in 6 months versus 12 months dialectical-behavior therapy for borderline personality disorder
Bibliographic record
Abstract
Objective We investigated whether defense mechanisms in patients with borderline personality disorder (BPD) predict treatment response of dialectical behavior therapy (DBT) and whether they moderate outcome in different treatment lengths.Method We analyzed a subsample of 60 outpatients with BPD, randomized into either 6 (n = 30) or 12 (n = 30) months of DBT. The average level of defensive adaptiveness, assessed with observer-rated overall defensive functioning (ODF) and “immature” (i.e., maladaptive) defenses were used as predictors and moderators of self-reported frequency of self-harm. We conducted a Generalized Linear Mixed Model (GLMM).Results A lower ODF at treatment onset predicted smaller reductions in self-harm, irrespective of treatment length (IRR = 0.92, 95% CI = [0.86, 0.99], p = .020). Lower order “immature” (“major image distorting”) defenses showed significantly smaller (IRR = 1.13, 95% CI = [1.06, 1.21], p < .001) and higher order “immature” (“minor image distorting”) defenses showed significantly larger (IRR = .91, 95% CI = [.85, .97], p = .006) reductions in self harm in the 6-month but not in the 12-month treatment.Conclusion Even though the results have to be regarded as preliminary due to the small sample size, findings might indicate that patients with BPD and lower average defensive adaptiveness may benefit from individualized treatment plans including specific interventions targeting defense function.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".