Effect of scalp acupuncture combined with computer-assisted training on memory impairment after stroke
Bibliographic record
Abstract
Objective To observe the effect of scalp acupuncture points including Shenting (GV24), Benshen (GB13) and Sishencong (EX-HN1) combined with computer-assisted training on memory impairment after stroke. Methods From May, 2023 to December, 2024, 62 patients with post-stroke memory dysfunction who received rehabilitation treatment in Beijing Bo'ai Hospital were selected and divided into control group (n = 31) and observation group (n = 31) randomly. Both groups received conventional treatment and computer-assisted training, while the observation group received additional scalp acupuncture treatment, for four weeks. They were evaluated with Montreal Cognitive Assessment (MoCA), auditory memory span, and modified Barthel Index (MBI) before and after treatment. Results No adverse reaction occured during treatment. After treatment, the total score of MoCA and the memory dimension score, and auditory memory span score improved in both groups (|t| ＞ 3.838, P ＜ 0.001), and the d-value was more in the observation group than in the control group (|t| ＞ 2.160, P ＜ 0.05); the score of MBI improved in both groups (|t| ＞ 7.471, P ＜ 0.001), however, there was no significant difference between two groups (P ＞ 0.05). Conclusion Computer-assisted training could significantly improve the cognitive function of patients with post-stroke memory dysfunction, especially the memory function, and is more effective while combining with scalp acupuncture.
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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 | 0.000 |
| 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".