The Effectiveness of Spinal Manipulative Therapy in Treating Spinal Pain Does Not Depend on the Application Procedures: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To assess whether spinal manipulative therapy (SMT) application procedures (ie, target, thrust, and region) impacted changes in pain and disability for adults with spine pain. DESIGN: Systematic review with network meta-analysis. LITERATURE SEARCH: We searched PubMed and Epistemonikos for systematic reviews indexed up to February 2022 and conducted a systematic search of 5 databases (MEDLINE, EMBASE, CENTRAL [Cochrane Central Register of Controlled Trials], PEDro [Physiotherapy Evidence Database], and Index to Chiropractic Literature) from January 1, 2018, to September 12, 2023. We included randomized controlled trials (RCTs) from recent systematic reviews and newly identified RCTs published during the review process and employed artificial intelligence to identify potentially relevant articles not retrieved through our electronic database searches. STUDY SELECTION CRITERIA: We included RCTs of the effects of high-velocity, low-amplitude SMT, compared to other SMT approaches, interventions, or controls, in adults with spine pain. DATA SYNTHESIS: The outcomes were spinal pain intensity and disability measured at short-term (end of treatment) and long-term (closest to 12 months) follow-ups. Risk of bias (RoB) was assessed using version 2 of the Cochrane RoB tool. Results were presented as network plots, evidence rankings, and league tables. RESULTS: We included 161 RCTs (11 849 participants). Most SMT procedures were equal to clinical guideline interventions and were slightly more effective than other treatments. When comparing inter-SMT procedures, effects were small and not clinically relevant. A general and nonspecific rather than a specific and targeted SMT approach had the highest probability of achieving the largest effects. Results were based on very low– to low-certainty evidence, mainly downgraded owing to large within-study heterogeneity, high RoB, and an absence of direct comparisons. CONCLUSION: There was low-certainty evidence that clinicians could apply SMT according to their preferences and the patients’ preferences and comfort. Differences between SMT approaches appear small and likely not clinically relevant. J Orthop Sports Phys Ther 2025;55(2):109-122. Epub 7 January 2025. https://doi.org/10.2519/jospt.2025.12707
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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 | 0.050 | 0.123 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.066 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".