Dialectical Behaviour Therapy Is an Effective Treatment for Depression and Anxiety in Multiple Sclerosis (P2.171)
Bibliographic record
Abstract
OBJECTIVE: To examine whether dialectical behaviour therapy (DBT), within the family of cognitive behavioural therapies, is effective for depressive and anxiety symptoms in multiple sclerosis (MS). BACKGROUND: Disorders of mood are frequently reported in MS, yet conclusions regarding the effectiveness of psychotherapeutic approaches are currently based on a small sample of controlled studies. DBT has a combined focus on acceptance and change strategies that are likely relevant for MS patients to aid in adjusting to a lifelong diagnosis while providing emotion regulation strategies. DESIGN/METHODS: Patients with MS were recruited from the MS clinic if they were positive on either the anxiety or depression subscales of the Hospital Anxiety and Depression Scale. A convenience sample of 20 patients were assigned to either DBT (n = 10) or standard care (n = 10). All patients were tested at pre- and post- treatment and at 6-month follow-up on measures of emotional and life function: Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), Hamilton Rating Scale for Depression (HRSD), Hamilton Rating Scale for Anxiety (HRSA), Symptom checklist-90-Revised (SCL-90-R), and MS Quality of Life-54 (MSQoL-54). Patients in the DBT group attended a skills training group twice weekly for 2 months for a total of 16 sessions. Nonparametric analyses using the Friedman test and Wilcoxon Signed Ranks Tests were conducted separately for each group. RESULTS: For the DBT group, significant improvements were demonstrated in self-rated and clinician-rated depressive symptoms (BDI-II and HRSD), clinician-rated anxiety (HRSA), self-rated general psychopathology symptoms (SCL-90-R), and quality of life (MSQoL-54). In contrast, the standard medical care group showed no significant improvements across all measures. CONCLUSIONS: This pilot work provides preliminary support for the utility of DBT as a means of improving emotional function in MS, but further work is needed the clarify this benefit using a larger randomized controlled approach.
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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) | 0.000 | 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) | 0.005 | 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".