A randomised pilot study comparing brief psychodynamic therapy with cognitive-behavioural therapy in the treatment of patients with fibromyalgia
Bibliographic record
Abstract
BACKGROUND: The present study mainly aimed to assess the effectiveness of Brief Psychodynamic Therapy (BPT) with respect to Cognitive-Behavioural Therapy (CBT) for the treatment of patients with Fibromyalgia (FM). As a secondary goal, we evaluated whether individual characteristics (attachment dimensions and alexithymia) contributed to predict treatment outcomes (anxiety and depression symptoms, and pain intensity). METHODS: A pilot randomised trial was conducted, with two repeated measures (at baseline - T0, and after the intervention - T1). Sixty-one female patients with FM were randomly allocated to the BPT group or CBT group and filled out the following questionnaires pre- and post-treatment: the Hospital Anxiety and Depression Scale (HADS), Fibromyalgia Impact Questionnaire (FIQ-R) Pain, Toronto Alexithymia Scale (TAS-20), and Relationship Questionnaire (RQ). RESULTS: Results of mixed-design ANOVAs revealed that there was an effect of time on both anxiety and pain symptoms scores, with a decrease in these symptoms from T0 to T1. Conversely, no group effect or time by group interaction was found. Multiple regression analyses showed that both the RQ "Dismissing" and TAS-20 total score were significant predictors of pain intensity after treatment. The TAS-20 total score was found to be the only predictor of HADS-Anxiety and HADS-Depression scores post-treatment. CONCLUSIONS: The present findings have shown that both psychotherapies seem to be equally effective in reducing anxiety symptoms and pain intensity in FM. Furthermore, both attachment and alexithymia were found to be significant predictors of pain intensity, whereas only alexithymia predicted anxiety and depression symptoms in the aftermath of 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".