Alterations in Cortical and Cerebellar Motor Processing in Subclinical Neck Pain Patients Following Spinal Manipulation
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was investigate whether there are alterations in cerebellar output in a subclinical neck pain (SCNP) group and whether spinal manipulation before motor sequence learning might restore the baseline functional relationship between the cerebellum and motor cortex. METHODS: Ten volunteers were tested with SCNP using transcranial magnetic stimulation before and after a combined intervention of spinal manipulation and motor sequence learning. In a separate experiment, we tested 10 healthy controls using the same measures before and after motor sequence learning. Our transcranial magnetic stimulation measurements included short-interval intracortical inhibition, long-interval intracortical inhibition, and cerebellar inhibition (CBI). RESULTS: The SCNP group showed a significant improvement in task performance as indicated by a 19% decrease in mean reaction time (P < .0001), which occurred concurrently with a decrease in CBI following the combined spinal manipulation and motor sequence learning intervention (F1,6 = 7.92, P < .05). The control group also showed an improvement in task performance as indicated by a 25% increase in reaction time (P < .001) with no changes to CBI. CONCLUSIONS: Subclinical neck pain patients have altered CBI when compared with healthy controls, and spinal manipulation before a motor sequence learning task changes the CBI pattern to one similar to healthy controls.
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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 | 0.001 |
| 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) | 0.002 | 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".