Potential mechanisms for lumbar spinal stiffness change following spinal manipulative therapy: a scoping review
Bibliographic record
Abstract
INTRODUCTION: In individuals having low back pain, the application of spinal manipulative therapy (SMT) has been shown to reduce spinal stiffness in those who report improvements in post-SMT disability. The underlying mechanism for this rapid change in stiffness is not understood presently. As clinicians and patients may benefit from a better understanding of this mechanism in terms of optimizing care delivery, the objective of this scoping review of current literature was to identify if potential mechanisms that explain this clinical response have been previously described or could be elucidated from existing data. METHODS: Three literature databases were systematically searched (MEDLINE, CINAHL, and PubMed). Our search terms included subject headings and keywords relevant to SMT, spinal stiffness, lumbar spine, and mechanism. Inclusion criteria for candidate studies were publication in English, quantification of lumbar spinal stiffness before and after SMT, and publication between January 2000 and June 2019. RESULTS: The search identified 1931 articles. Of these studies, 10 were included following the application of the inclusion criteria. From these articles, 7 themes were identified with respect to potential mechanisms described or derived from data: 1) change in muscle activity; 2) increase in mobility; 3) decrease in pain; 4) increase in pressure pain threshold; 5) change in spinal tissue behavior; 6) change in the central nervous system or reflex pathways; and 7) correction of a vertebral dysfunction. CONCLUSIONS: This scoping review identified 7 themes put forward by authors to explain changes in spinal stiffness following SMT. Unfortunately, none of the studies provided data which would support the promotion of one theme over another. As a result, this review suggests a need to develop a theoretical framework to explain rapid biomechanical changes following SMT to guide and prioritize future investigations in this important clinical area.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".