Effects of scalp acupuncture combined with repetitive transcranial magnetic stimulation on post-stroke cognitive impairment at different time intervals
Bibliographic record
Abstract
OBJECTIVE: To investigate the therapeutic efficacy of scalp acupuncture combined with repetitive transcranial magnetic stimulation (rTMS) for post-stroke cognitive impairment at different time intervals. METHOD: This retrospective cohort study divided patients into two groups according to the timing of the scalp acupuncture combined with rTMS intervention. Group A received scalp acupuncture combined with rTMS at 1 month post-stroke and routine basic treatment and cognitive function training at two months post-stroke. Group B received routine basic treatment and cognitive function training at 1 month post-stroke and scalp acupuncture combined with rTMS at 2 months post-stroke. Both groups underwent cognitive assessment using the Montreal Cognitive Assessment (MoCA) before treatment and at the ends of the first and second months post-stroke. RESULTS: The study population included 92 total stroke patients divided evenly into Groups A and B. Group A's total scores were higher at the end of the first month of treatment compared with baseline and remained stable at the end of the second month of treatment. By contrast, Group B's total score remained stable at the end of the first month of treatment compared with baseline and increased by the end of the second month. There were no significant differences in the scores at baseline or the end of the second month between the two groups. CONCLUSION: Scalp acupuncture combined with rTMS can effectively treat cognitive function in patients with post-stroke cognitive impairment, regardless of the timing of the intervention.
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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 | 0.001 |
| 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".