Physiological Responses to Spinal Manipulation Therapy: Investigation of the Relationship Between Electromyographic Responses and Peak Force
Bibliographic record
Abstract
OBJECTIVE: It is believed that systematic modulation of spinal manipulative therapy (SMT) parameters should yield varying levels of physiological responses and eventually a range of clinical responses. However, investigation of SMT dose-physiological response relationship is recent and has mostly been conducted using animal or cadaveric models. The main objective of the present study is to investigate SMT dose-physiological response relation in humans by determining how different levels of force can modify electromyographic (EMG) responses to spinal manipulation. METHODS: Twenty-six participants were subjected to 2 trials of 4 different SMT force-time profiles using a servo-controlled linear actuator motor. Normalized EMG activity of paraspinal muscles (left and right muscles at level T6 and T8) was recorded during and after SMT, and EMG values were compared across the varying levels of force. RESULTS: Increasing the level of force yielded an increase in paraspinal muscle EMG activity during the thrust phase of SMT but also in the two 250-millisecond time windows after the spinal manipulation impulse. These muscle activations quickly attenuated (500 milliseconds after spinal manipulation impulse). CONCLUSION: The study confirmed the presence of a local paraspinal EMG response after SMT and highlighted the linear relationship between the SMT peak force and paraspinal muscle activation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".