Moderators of the Effect of Spinal Manipulative Therapy on Pain Relief and Function in Patients with Chronic Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Individual participant data (IPD) meta-analysis. OBJECTIVE: The aim of this study was to identify which participant characteristics moderate the effect of spinal manipulative therapy (SMT) on pain and functioning in chronic LBP. SUMMARY OF BACKGROUND: The effects of SMT are comparable to other interventions recommended in guidelines for chronic low back pain (LBP); however, it is unclear which patients are more likely to benefit from SMT compared to other therapies. METHODS: IPD were requested from randomized controlled trials (RCTs) examining the effect of SMT in adults with chronic LBP for pain and function compared to various other therapies (stratified by comparison). Potential patient moderators (n = 23) were a priori based on their clinical relevance. We investigated each moderator using a one-stage approach with IPD and investigated this interaction with the intervention for each time point (1, 3, 6, and 12 months). RESULTS: We received IPD from 21 of 46 RCTs (n = 4223). The majority (12 RCTs, n = 2249) compared SMT to recommended interventions. The duration of LBP, baseline pain (confirmatory), smoking, and previous exposure to SMT (exploratory) had a small moderating effect across outcomes and follow-up points; these estimates did not represent minimally relevant differences in effects; for example, patients with <1 year of LBP demonstrated more positive point estimates for SMT versus recommended therapy for the outcome pain (mean differences ranged from 4.97 (95% confidence interval, CI: -3.20 to 13.13) at 3 months, 10.76 (95% CI: 1.06 to 20.47) at 6 months to 5.26 (95% CI: -2.92 to 13.44) at 12 months in patients with over a year LBP. No other moderators demonstrated a consistent pattern across time and outcomes. Few moderator analyses were conducted for the other comparisons because of too few data. CONCLUSION: We did not identify any moderators that enable clinicians to identify which patients are likely to benefit more from SMT compared to other treatments.Level of Evidence: 2.
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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 | 0.055 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.059 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".