Yoga for Chronic Low Back Pain: A Meta‐Analysis of Randomized Controlled Trials
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To evaluate the efficacy of yoga as an intervention for chronic low back pain (CLBP) using a meta-analytical approach. Randomized controlled trials (RCTs) that examined pain and⁄or functional disability as treatment outcomes were included. Post-treatment and follow-up outcomes were assessed. METHODS: A comprehensive search of relevant electronic databases, from the time of their inception until November 2011, was conducted. Cohen's d effect sizes were calculated and entered in a random-effects model. RESULTS: Eight RCTs met the criteria for inclusion (eight assessing functional disability and five assessing pain) and involved a total of 743 patients. At post-treatment, yoga had a medium to large effect on functional disability (d=0.645) and pain (d=0.623). Despite a wide range of yoga styles and treatment durations, heterogeneity in post-treatment effect sizes was low. Follow-up effect sizes for functional disability and pain were smaller, but remained significant (d=0.397 and d=0.486, respectively); however, there was a moderate to high level of variability in these effect sizes. DISCUSSION: The results of the present study indicate that yoga may be an efficacious adjunctive treatment for CLBP. The strongest and most consistent evidence emerged for the short-term benefits of yoga on functional disability. However, before any definitive conclusions can be drawn, there are a number of methodological concerns that need to be addressed. In particular, it is recommended that future RCTs include an active control group to determine whether yoga has specific treatment effects and whether yoga offers any advantages over traditional exercise programs and other alternative therapies for CLBP.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.137 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.015 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.080 | 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 it