Correlated Change in Upper Limb Function and Motor Cortex Activation After Verum and Sham Acupuncture in Patients with Chronic Stroke
Bibliographic record
Abstract
BACKGROUND: Acupuncture may improve motor function in patients with chronic hemiparetic stroke, yet the neural mechanisms underlying such an effect are unknown. As part of a sham-controlled, randomized clinical trial testing the efficacy of a 10-week acupuncture protocol in patients with chronic hemiparetic stroke, we examined the relationship between changes in function of the affected upper limb and brain activation using functional magnetic resonance imaging (fMRI). METHODS: Seven (7) chronic hemiparetic stroke patients underwent fMRI and testing of function of the affected upper limb (spasticity and range-of-motion) before and after a 10-week period of verum (N=4) or sham (N=3) acupuncture. The correlation between changes in function of the affected upper limb and brain activation after treatment was tested across patients. RESULTS: We found a significant positive correlation between changes in function of the affected upper limb (spasticity and range of motion) and activation in a region of the ipsilesional motor cortex. Patients treated with verum acupuncture showed a trend toward a greater maximum activation change in this motor cortical area as compared to those treated with sham acupuncture. CONCLUSIONS: Acupuncture may improve function of the affected upper limb in chronic hemiparetic stroke patients by increasing activity in the ipsilesional motor cortex.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".