Changes in comorbid depression following intensive trauma-focused treatment for PTSD and complex PTSD
Bibliographic record
Abstract
BACKGROUND: The extent to which intensive trauma-focused treatment for individuals with post-traumatic stress disorder (PTSD) is also effective in treating comorbid major depressive disorder (MDD) remains unclear. OBJECTIVE: The purpose of the present study was to test the hypothesis that brief intensive trauma-focused therapy for PTSD is associated with significant reductions in depressive symptoms and loss of diagnostic status of MDD. METHODS: A total of 334 adult patients with PTSD (189 patients who were also diagnosed with MDD) underwent a brief intensive trauma-focused treatment programme consisting of EMDR therapy, prolonged exposure, physical activity, and psychoeducation. At pre-treatment, post-treatment and 6-month follow-up, severity and diagnostic status of PTSD and MDD were assessed. A linear mixed model was used to analyze changes in the severity of PTSD and depressive symptoms, whereas a generalized linear mixed model was used to determine changes in the MDD diagnostic status. RESULTS: = 1.67 and 0.73, respectively). The proportion of patients fulfilling the diagnostic status of MDD changed from 57% at pre-treatment to 33% at the 6-month follow-up. Although the initial response to treatment did not differ between patients with and without comorbid MDD, for both groups a significant relapse in depressive symptoms was found after six months, which could be explained almost entirely by the presence of CPTSD at baseline. CONCLUSIONS: The results support the notion that brief, intensive trauma-focused treatment is highly effective for individuals with PTSD and comorbid MDD. Because patients with CPTSD are vulnerable to relapse in depressive symptoms, this target group may require additional treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".