The effects of reciprocal inhibition on motor function and brain functional network connectivity of stroke patients
Bibliographic record
Abstract
Objective To investigate the effects of reciprocal inhibition on motor function connectivity in the brains of stroke patients.Methods Thirty patients with stroke were randomly divided into a treatment group (n =15) and a control group (n =15).The control group underwent normal limb positioning,medium frequency electrotherapy,circulated compression of the limbs,etc.The treatment group received conventional rehabilitation treatment plus reciprocal inhibition treatment for 30 min daily,6 times a week for 4 weeks.All of the patients were assessed before and after treatment using the Canadian neurological scale (CNS),the Frenchay activities index (FAI),the motricity index (MI) and functional magnetic resonance imaging of the motor cortex in a resting state (rs-fMRI).Results In both groups the average CNS,FAI and MI scores improved significantly.Compared with the control group,the changes in FAI and MI scores in the treatment group improved significantly more.The coefficient of functional connectivity of the bilateral motor cortex decreased significantly after treatment in both groups.In the treatment group the motor cortex functional connectivity correlated significantly with the improvements in MI scores.Conclusions Reciprocal inhibition can accelerate the improvement of extremity motor function and ability in the activities of daily living significantly after stroke.It reduces functional connectivity in the bilateral motor cortex in ways significantly correlated with improvements in motor function. Key words: Reciprocal inhibition; Resting-state functional magnetic resonance imaging; Stroke
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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) | 0.001 | 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".