The effect on clinical outcomes when targeting spinal manipulation at stiffness or pain sensitivity: a randomized trial
Bibliographic record
Abstract
The mechanisms underlying pain relief following spinal manipulative therapy (SMT) are not understood fully although biomechanical and neurophysiological processes have been proposed. As such, we designed this randomized trial to elucidate the contributions of biomechanical and neurophysiological processes. A total of 132 participants with low back pain were randomly assigned to receive SMT at either the lumbar segment measured as the stiffest or the segment measured as having the lowest pain threshold. The primary outcome was patient reported low back pain intensity following treatment. Secondary outcomes were biomechanical stiffness and neurophysiological pressure pain threshold. All outcomes were measured at baseline, after the fourth and final session and at 2-weeks follow-up. Data were analyzed using linear mixed models, and demonstrated that the SMT application site did not influence patient reported low back pain intensity or stiffness. However, a large and significant difference in pressure pain threshold was observed between groups. This study provides support that SMT impacts neurophysiological parameters through a segment-dependent neurological reflex pathway, although this do not seem to be a proxy for improvement. This study was limited by the assumption that the applied treatment was sufficient to impact the primary outcome.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".