[Acupuncture of revised acupoint combination around the skull base for post-stroke mild cognitive impairment: a randomized controlled trial].
Bibliographic record
Abstract
OBJECTIVE: To observe the clinical efficacy of acupuncture of revised acupoint combination around the skull base in treating post-stroke mild cognitive impairment (PSMCI), and preliminary explore its action mechanism. METHODS: A total of 76 PSMCI patients were randomly divided into an observation group (38 cases, 4 cases dropped off) and a control group (38 cases, 3 cases dropped off, 1 case was removed). In the observation group, acupuncture of revised acupoint combination around the skull base (bilateral Fengchi [GB 20], Wangu [GB 12], Tianzhu [BL 10] and Yamen [GV 15], Baihui [GV 20]) was used for treatment. In the control group, 8 non-meridian and non-acupoint points at the distal end were selected for shallow puncture treatment. Retaining the needles of 30 min, once every other day,3 times a week for 4 weeks in both groups. The scores of Montreal cognitive assessment (MoCA), mini-mental state examination (MMSE), Barthel index (BI) and serum levels of cystatin C (Cys-C) and homocysteine (Hcy) were compared in the two groups before and after treatment, and the clinical efficacy was evaluated. RESULTS: <0.05). CONCLUSION: Acupuncture of revised acupoint combination around the skull base can improve cognitive function and daily living ability of PSMCI patients, which may be related to the down regulation of serum levels of Cys-C and Hcy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".