Electroacupuncture combined with cognitive rehabilitation outperforms cognitive rehabilitation alone in treating post-stroke cognitive impairment: a randomized controlled trial
Bibliographic record
Abstract
This study aimed to evaluate the effects of a 12-week intervention combining electroacupuncture (EA) with conventional cognitive rehabilitation (CR) on cognitive recovery and brain network topological properties in stroke patients. Thirty-four post-stroke cognitive impairment (PSCI) patients were randomly assigned to either an EA + CR group or a CR-only group, with both undergoing five weekly interventions for 12 weeks. Cognitive assessments and Diffusion Tensor Imaging tests were performed pre- and post-intervention. Primary outcomes included the Montreal Cognitive Assessment (MoCA), Digit Span Test (DST), Auditory Verbal Learning Test (AVLT-H), and Aphasia Screening Scale. Secondary outcomes measured brain network global and nodal properties. The EA + CR group showed significant improvements in MoCA (p < 0.001), AVLT-H (p = 0.004), and in areas like naming (p = 0.022), attention (p < 0.001), and delayed recall (p < 0.001). Local brain network metrics, such as clustering coefficients (Cp) and local efficiency (Eloc), were significantly higher (p < 0.05) in the EA + CR group, though global efficiency (Eglob) was unchanged. Improvements in Eloc were positively correlated with MoCA scores (r = 0.0716, p = 0.018). Specific brain regions, including the medial and paracentral cingulate gyrus, inferior temporal gyrus, and left supramarginal gyrus, exhibited significant nodal property differences, suggesting that EA may enhance cognitive recovery by targeting cognitively relevant regions. These findings indicate that EA, combined with CR, is a safe and effective treatment for cognitive impairment following stroke, potentially via structural brain network improvements. Clinical trial registration https://clinicaltrials.gov/ , ChiCTR2200066160.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".