Self-Management Programs for Chronic Non-Cancer Pain: A Rapid Review of Randomized Trials
Bibliographic record
Abstract
ABSTRACT: Background: The body of evidence regarding self-management programs (SMPs) for adult chronic non-cancer pain (CNCP) is steadily growing, and regular updates are needed for effective decision-making. Objectives: To systematically identify, critically appraise, and summarize the findings from randomized controlled trials (RCTs) of SMPs for CNCP. Methods: We searched relevant databases from 2009 to August 2021 and included English-language RCT publications of SMPs compared with usual care for CNCP among adults (18+ years old). The primary outcome was health-related quality of life (HR-QoL). We conducted meta-analysis using an inverse variance, random-effects model and calculated the standardized mean difference (SMD) and associated 95% confidence interval (CI) and statistical heterogeneity using the I2 statistic. Results: From 8538 citations, we included 28 RCTs with varying patient populations, standards for SMPs, and usual care. No RCTs were classified as having a low risk of bias. There was no evidence of a significant improvement in overall HR-QoL, irrespective of pain type, immediately post-intervention (SMD 0.01, 95%CI −0.21 to 0.24; I2 57%; 11 RCTs; 979 participants), 1–4 months post-intervention (SMD 0.02, 95%CI −0.16 to 0.20; I2 48.7%; 12 RCTs; 1160 participants), and 6–12 months post-intervention (SMD 0.07, 95%CI −0.06 to 0.21; I2 26.1%; 9 RCTs; 1404 participants). Similar findings were made for physical and mental HR-QoL, and for specific QoL assessment scales (e.g., SF-36). Conclusions: There is a lack of evidence that SMPs are efficacious for CNCP compared with usual care. Standardization of SMPs for CNCP and better planned/conducted RCTs are needed to confirm these conclusions.
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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 | 0.044 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".