Cost-effectiveness of Multidisciplinary Interventions for Chronic Low Back Pain
Bibliographic record
Abstract
OBJECTIVE: Chronic musculoskeletal pain in adults is a global health and economic problem. The aim of this paper was to systematically review and determine what proportion of multidisciplinary approaches to managing chronic musculoskeletal pain are cost-effective. MATERIALS AND METHODS: The EconLit, Embase, and PubMed electronic databases were searched for randomized and nonrandomized economic evaluation studies of nonpharmaceutical multidisciplinary chronic pain management interventions published from inception through to August 2019. RESULTS: Seven studies comprising 2095 patients were included. All studies involved diverse multidisciplinary teams in one or more of the study arms. All studies involved chronic (both chronic and subacute) low back pain and were economic evaluations from either a societal or health care perspective. Two of the 3 studies that reported on a multidisciplinary pain intervention compared with nonmultidisciplinary intervention concluded favorable cost-effectiveness based on cost per quality adjusted life years gained, 1 study was not found to be cost-effective. Cost-effectiveness of the multidisciplinary intervention of interest was also not established by another 3-arm study. Two studies compared 2 multidisciplinary interventions; neither of these could definitively declare cost-effectiveness. The remaining study indicated the intervention by a multidisciplinary team was more effective but at a higher cost. None of the included studies used decision models to estimate long-term health outcomes and cost-effectiveness of multidisciplinary programs. DISCUSSION: There are few studies on the cost-effectiveness of multidisciplinary chronic pain management interventions. This study encourages additional rigorous economic evaluations of multidisciplinary models for chronic pain management. Economic evaluations that enable extrapolating costs and effects of multidisciplinary programs beyond the time horizon of clinical trials may be more informative for clinicians and health administrators.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".