Effects of Transcranial Alternating Current Stimulation Combined with Electroacupuncture on Patients with Attention Deficit after Stroke
Bibliographic record
Abstract
ObjectiveTo investigate the effects of transcranial alternating current stimulation (tACS) combined with electroacupuncture on attention function and walking ability in stroke patients with attention deficit.MethodsA total of 60 stroke patients with attention deficit treated in the Department of Rehabilitation Medicine at Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine from January to December 2023 were randomly assigned to control group and observation group using a random number table generated by SPSS 26.0 statistical software, with 30 cases in each group. One patient in the control group voluntarily withdrew, and one in the observation group dropped out due to early hospital discharge. The control group received tACS treatment and sham EA stimulation, with a tACS treatment frequency of 6 Hz, current of 1 mA, and intervention duration of 20 minutes a time. Sham EA stimulation was applied at Shenting (DU24) and Baihui (DU20) acupoints, with acupuncture needles stimulating only the epidermis without skin penetration or electrical current, and the stimulation duration was 20 minutes a time. The observation group received tACS treatment and EA stimulation. The tACS treatment was the same as that in the control group. EA stimulation was applied at Shenting (DU24) and Baihui (DU20) acupoints, with acupuncture needles inserted at a 30° angle to the scalp. Electroacupuncture used dense-disperse waves with a frequency of 2/10 Hz and current intensity adjusted to the subject's tolerance, and each intervention lasted 20 minutes. Both groups received treatment once daily, five times per week, for a total of two weeks. Before and after treatment, the Montreal Cognitive Assessment (MoCA) was used to assess the cognitive function. The MoCA attention score, trail making test A (TMT-A) and trail making test B (TMT-B) were used to assess the attention function. Functional Ambulation Category Scale (FAC) was used to assess the walking ability. A digital monitoring treadmill was used to assess gait kinematics (hip and knee joint range of motion). The correlation between changes in walking ability scores and cognitive attention function scores was analyzed.Results(1) MoCA total score and attention function score: compared with those before treatment, the MoCA score and MoCA attention score in both groups after treatment increased significantly (P<0.05), and the TMT-A score and TMT-B score decreased significantly (P<0.05). Compared with the control group, the MoCA score and MoCA attention score in the observation group after treatment were significantly higher (P<0.05), and the TMT-B score was significantly lower (P<0.05). (2) FAC score and hip/knee joint range of motion: compared with those before treatment, the FAC score, hip joint range of motion and knee joint range of motion in both groups after treatment increased significantly (P<0.05). Compared with the control group, the FAC score and hip joint range of motion in the observation group after treatment were significantly higher (P<0.05), while the difference was not statistically significant in knee joint range of motion (P>0.05). (3) Correlation between walking ability difference and cognitive attention difference: there was positive correlation between the FAC difference and the MoCA score difference (r=0.333, P<0.05), and the FAC difference was positively correlated with the difference in MoCA attention score (r=0.308, P<0.05); the difference in hip joint range of motion was positively correlated with the difference in MoCA score (r=0.425, P<0.05), with the difference in MoCA attention score (r=0.442, P<0.05), and with the difference in TMT-A score (r=0.2931, P<0.05).ConclusiontACS combined with EA can improve the attention function and walking ability of stroke patients with attention deficit, and the walking ability improvement is closely related to cognitive and attentional functions.
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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) | 0.001 | 0.001 |
| 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".