Biopsychosocial Approaches to the Treatment of Chronic Pain
Bibliographic record
Abstract
BACKGROUND: Biopsychosocial treatments address the range of physical, psychological, and social components of chronic pain. OBJECTIVE: This review sought to determine how effective unimodal and multimodal biopsychosocial approaches are in the treatment of chronic pain. METHODOLOGY: The literature search identified three systematic reviews of the literature and 21 randomized controlled trials to provide the evidence for this review. RESULTS: The systematic reviews and 12 randomized controlled trials reported on chronic low back pain. Other randomized controlled trials studied fibromyalgia (three trials) and back or other musculoskeletal disorders (five trials). Biopsychosocial components reviewed were electromyogram feedback and hypnosis as unimodal approaches, and behavioral and cognitive-behavioral treatments and back school, or group education, as multimodal approaches for chronic low back pain. For other chronic pain disorders, cognitive-behavioral treatments were reviewed. Comparisons were hindered by studies with heterogeneous subjects, varied comparison groups, different cointerventions and follow-up times, variable outcomes, and a range of analytic methods. CONCLUSIONS: Multimodal biopsychosocial treatments that include cognitive-behavioral and/or behavioral components are effective for chronic low back pain and other musculoskeletal pain for up to 12 months (level 2). There is limited evidence (level 3) that electromyogram feedback is effective for chronic low back pain for up to 3 months. The remaining evidence of longer-term effectiveness and of effectiveness of other interventions was inadequate (level 4a) or contradictory (level 4b). Future studies of cognitive-behavioral treatments should be condition specific, rather than include patients with different pain conditions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".