[Impacts of acupuncture at Jing-well points on the differentiated meridians and temple-three-needle therapy on P300 of patients with early vascular cognitive impairment].
Bibliographic record
Abstract
OBJECTIVE: To observe the efficacy on post-stroke mild cognitive impairment (MCI) treated with acupuncture at Jing-well points on the differentiated meridians and temple-three-needle therapy. METHODS: Seventy-three of stroke patients were randomized into an acupuncture group (37 cases) and a conventional treatment group (36 cases). Twenty healthy aged people in physical examination were collected as a control group. In the acupuncture group, on the basis of the conventional treatment of internal medication, the acupuncture at Jing-well points on the differentiated meridians and temple-three-needle therapy were applied. In the conventional treatment group, no any therapy was used except the conventional treatment of internal medication. In the control group, no any intervention was adopted. Neuroscan Nuamps electroencephalogram recording analysis system was used to determine the event-related potentials P300, and the amplitude and mini mental state examination (MMSE) score was observed before and after treatment in both groups. RESULTS: After treatment, in the acupuncture group, P300 latent stage was shortened, and the amplitude and the score of MMSE were increased (P < 0.05, P < 0.01). In the conventional treatment group, above indices were not changed obviously as compared with that before treatment (all P > 0.05). Compared with the conventional treatment group, the differences in P300 latent stage, amplitude and MMSE score were remarkable in the acupuncture group (P < 0.05, P < 0.01). CONCLUSION: The acupuncture at Jing-well points on the differentiated meridians and temple-three-needle therapy improves the cognitive function of the patients with MCI.
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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) | 0.000 | 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".