[<i>Tongdu Xingshen</i> acupuncture and moxibustion combined with cognitive training in treatment of post-stroke mild cognitive impairment: a randomized controlled trial].
Bibliographic record
Abstract
OBJECTIVE: (promoting the governor vessel and regaining consciousness) acupuncture and moxibustion combined with cognitive training and the simple cognitive training for post-stroke mild cognitive impairment (PSMCI). METHODS: acupuncture and moxibustion combined with cognitive training, acupuncture was given at Baihui (GV 20), Sishencong (EX-HN 1), Shenting (GV 24), etc., and moxibustion was given at Shenting (GV 24) , Baihui (GV 20), Shendao (GV 11), Fengfu (GV 16) and Xinshu (BL 15). The control group was only given cognitive training. All the above treatment was given once a day, 5 times a week, for 4 consecutive weeks. The scores of Montreal cognitive assessment (MoCA), mini-mental state examination (MMSE), activity of daily living (ADL) and stroke-specific quality of life (SS-QOL) were compared between the two groups before treatment, after treatment, 4 weeks and 12 weeks after treatment. RESULTS: <0.05). CONCLUSION: acupuncture and moxibustion combined with cognitive training and simple cognitive training can improve cognitive function, daily living ability and quality of life in patients with PSMCI, and the combined therapy is superior to simple cognitive training in improving cognitive function and long-term quality of life in patients with PSMCI.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".