Regional Sensorimotor Effects of Chiropractic Spinal Manipulation: Preliminary Results From an Experimental Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to assess the effects of different spinal manipulation (SM) techniques and target segments on a specific dermatome and myotome, when compared with a remote spinal cord segment that served as a control location. METHODS: Twenty-nine healthy volunteers were randomized to receive instrumental (Activator IV, Activator Methods International Ltd) or manual SM at the C6, C1, and T4 vertebral segments in 3 independent sessions. Pressure pain thresholds (PPTs) and muscle strength were examined at the C6 (test) and L4 (control) dermatomes and myotomes, at baseline and after intervention. Linear mixed-effects models were used to analyze changes over time and interindividual variability. RESULTS: Pressure pain thresholds significantly increased at both proximal and distal C6 dermatome locations (P < .05), irrespective of the technique and segment of application (P > .2). No significant changes were observed at the L4 dermatome. Muscle strength remained unchanged throughout the study. Multilevel modeling revealed significant associations between increased PPTs along the C6 dermatome (P < .001), whereas the combination of technique and target segment predicted PPT increases at the proximal C6 dermatome. CONCLUSION: These findings support regional, rather than segmental mechanisms underlying the sensory effects of SM. Specifically, significant increases in PPTs along the C6 dermatome suggest localized effects on pain sensitivity, which may depend on the target spinal region. Further investigation is needed to better understand these regional changes of SM and their potential clinical implications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".