The effect of spinal manipulative therapy on pain relief and function in patients with chronic low back pain: an individual participant data meta-analysis
Bibliographic record
Abstract
BACKGROUND: A 2019 review concluded that spinal manipulative therapy (SMT) results in similar benefit compared to other interventions for chronic low back pain (LBP). Compared to traditional aggregate analyses individual participant data (IPD) meta-analyses allows for a more precise estimate of the treatment effect. PURPOSE: To assess the effect of SMT on pain and function for chronic LBP in a IPD meta-analysis. DATA SOURCES: Electronic databases from 2000 until April 2016, and reference lists of eligible trials and related reviews. STUDY SELECTION: Randomized controlled trials (RCT) examining the effect of SMT in adults with chronic LBP compared to any comparator. DATA EXTRACTION AND DATA SYNTHESIS: We contacted authors from eligible trials. Two review authors independently conducted the study selection and risk of bias. We used GRADE to assess the quality of the evidence. A one-stage mixed model analysis was conducted. Negative point estimates of the mean difference (MD) or standardized mean difference (SMD) favors SMT. RESULTS: Of the 42 RCTs fulfilling the inclusion criteria, we obtained IPD from 21 (n=4223). Most trials (s=12, n=2249) compared SMT to recommended interventions. There is moderate quality evidence that SMT vs recommended interventions resulted in similar outcomes on pain (MD -3.0, 95%CI: -6.9 to 0.9, 10 trials, 1922 participants) and functional status at one month (SMD: -0.2, 95% CI -0.4 to 0.0, 10 trials, 1939 participants). Effects at other follow-up measurements were similar. Results for other comparisons (SMT vs non-recommended interventions; SMT as adjuvant therapy; mobilization vs manipulation) showed similar findings. SMT vs sham SMT analysis was not performed, because we only had data from one study. Sensitivity analyses confirmed these findings. LIMITATIONS: Only 50% of the eligible trials were included. CONCLUSIONS: Sufficient evidence suggest that SMT provides similar outcomes to recommended interventions, for pain relief and improvement of functional status. SMT would appear to be a good option for the treatment of chronic LBP. Systematic Review Registration Number PROSPERO CRD42015025714.
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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 | 0.052 | 0.110 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.070 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".