Electromyographic Responses of Neck, Back, and Limb Outlet Muscles Associated With High-Velocity, Low-Amplitude Manual Cervical and Upper Thoracic Spinal Manipulation of Individuals With Mild Neck Disability: A Descriptive Observational Investigation
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to investigate the extent of electromyographic responses associated with manual high-velocity, low-amplitude (HVLA) spinal manipulation systematically applied to the upper and lower cervical and upper thoracic spines in a cohort with mild neck disability. METHODS: The study was a descriptive observational investigation, with all participants receiving the same interventions. Nineteen participants with mild neck disability received 6 manual HVLA manipulations to the cervical and upper thoracic spine. Bipolar surface electromyography electrode pairs were used to measure responses of 16 neck, back, and limb outlet muscles bilaterally. The number of electromyographic responses was then calculated. RESULTS: Electromyographic responses associated with cervical and thoracic manipulation occurred in a median of 4 of the 16 (range: 1-14) recorded muscles. Cervical spinal manipulation was associated with the highest rates of electromyographic responses in neck muscles, whereas responses in back muscles were highest after upper thoracic manipulation. CONCLUSION: Cervical spinal manipulation was associated with the highest rate of electromyographic responses in muscles of the cervical spine (sternocleidomastoid and splenius cervicis), whereas responses in back muscles (upper and middle trapezius, latissimus dorsi, and longissimus thoracis) were highest after upper thoracic manipulations. This result suggests that electromyographic muscular responses associated with spinal manipulation primarily occur locally (close to the target segment) rather than distally.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".