[Interactive scalp acupuncture for cognitive dysfunction after stroke: a randomized controlled trial].
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of interactive scalp acupuncture, scalp acupuncture alone and scalp acupuncture plus cognitive training for cognitive dysfunction after stroke. METHODS: A total of 660 patients with cognitive dysfunction after stroke were randomly divided into an interactive scalp acupuncture group (218 cases, 18 cases dropped off), a scalp acupuncture group (220 cases, 20 cases dropped off) and a scalp acupuncture plus cognitive training group (222 cases, 22 cases dropped off). All the patients were treated with routine medication and exercise rehabilitation training. The interactive scalp acupuncture group was treated with scalp acupuncture on the parietal midline, and contralateral anterior parietal temporal oblique line and posterior parietal temporal oblique line at the same time of cognitive training; the scalp acupuncture group was treated with scalp acupuncture alone, and the scalp acupuncture plus cognitive training group was treated with scalp acupuncture and cognitive training in the morning and afternoon respectively. All the treatments were given once a day, 6 times a week for 8 weeks. Montreal cognitive assessment (MoCA) scale score was used to evaluate the cognitive function before treatment, 4 weeks and 8 weeks into treatment. RESULTS: <0.01). CONCLUSION: The interactive scalp acupuncture could significantly improve the cognitive function in patients with cognitive dysfunction after stroke, and the efficacy is superior to scalp acupuncture alone and scalp acupuncture plus cognitive training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".