Cognitive behavioural therapies for fibromyalgia
Bibliographic record
Abstract
BACKGROUND: Fibromyalgia (FM) is a clinically well-defined chronic condition of unknown aetiology characterized by chronic widespread pain that often co-exists with sleep disturbances, cognitive dysfunction and fatigue. Patients often report high disability levels and negative mood. Psychotherapies focus on reducing key symptoms, improving daily functioning, mood and sense of personal control over pain. OBJECTIVES: To assess the benefits and harms of cognitive behavioural therapies (CBTs) for treating FM at end of treatment and at long-term (at least six months) follow-up. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2013, Issue 8), MEDLINE (1966 to 28 August 2013), PsycINFO (1966 to 28 August 2013) and SCOPUS (1980 to 28 August 2013). We searched http://www.clinicaltrials.gov (web site of the US National Institutes of Health) and the World Health Organization Clinical Trials Registry Platform (ICTRP) (http://www.who.int/ictrp/en/) for ongoing trials (last search 28 August,2013), and the reference lists of reviewed articles. SELECTION CRITERIA: We selected randomised controlled trials of CBTs with children, adolescents and adults diagnosed with FM. DATA COLLECTION AND ANALYSIS: The data of all included studies were extracted and the risks of bias of the studies were assessed independently by two review authors. Discrepancies were resolved by discussion. MAIN RESULTS: Twenty-three studies with 24 study arms with CBTs were included. A total of 2031 patients were included; 1073 patients in CBT groups and 958 patients in control groups. Only two studies were without any risk of bias. The GRADE quality of evidence of the studies was low. CBTs were superior to controls in reducing pain at end of treatment by 0.5 points on a scale of 0 to 10 (standardised mean difference (SMD) - 0.29; 95% confidence interval (CI) -0.49 to -0.17) and by 0.6 points at long-term follow-up (median 6 months) (SMD -0.40; 95% CI -0.62 to -0.17); in reducing negative mood at end of treatment by 0.7 points on a scale of 0 to 10 (SMD - 0.33; 95% CI -0.49 to -0.17) and by 1.3 points at long-term follow-up (median 6 months) (SMD -0.43; 95% CI -0.75 to -0.11); and in reducing disability at end of treatment by 0.7 points on a scale of 0 to 10 (SMD - 0.30; 95% CI -0.51 to -0.08) and at long-term follow-up (median 6 months) by 1.2 points (SMD -0.52; 95% CI -0.86 to -0.18). There was no statistically significant difference in dropout rates for any reasons between CBTs and controls (risk ratio (RR) 0.94; 95% CI 0.65 to 1.35). AUTHORS' CONCLUSIONS: CBTs provided a small incremental benefit over control interventions in reducing pain, negative mood and disability at the end of treatment and at long-term follow-up. The dropout rates due to any reason did not differ between CBTs and controls.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".