Effect of Traditional Chinese Medicine Baduanjin exercise combined with remote ischemic conditioning on mild cognitive impairment after stroke
Bibliographic record
Abstract
Objective To investigate the intervention effect of Traditional Chinese Medicine(TCM) Baduanjin exercise combined with remote ischemic conditioning therapy for patients with mild cognitive impairment in stroke. Methods Seventy patients with mild cognitive impairment after stroke were recruited as study subjects and randomly divided into control group(n=35) and intervention group(n=35). Patients in the control group received conventional treatment and care, and those in the intervention group received TCM Baduanjin exercise combined with remote ischemic conditioning therapy on the basis of conventional care. The cognitive function, daily living ability and quality of life of the two groups were compared before and after 12 weeks of intervention. Results After 12-week intervention, the scores of Montreal Cognitive Assessment (MoCA), Modified Barthel Index (MBI) and Stroke-specific Quality of Life Scale (SS-QOL) were significantly better than those before the intervention in both groups (P＜0. 01), and scores of the intervention group were significantly higher than those of control group (P＜0. 01). Conclusion TCM Baduanjin exercise combined with remote ischemic conditioning therapy is safe and effective for patients with mild cognitive impairment after stroke, and can improve overall cognitive function, daily living ability and quality of life. (目的 探讨脑卒中后轻度认知障碍患者应用中医传统功法八段锦联合远隔缺血适应治疗的干预效果。方法 选取2021年6月—2022年6月医院收治的70例脑卒中后轻度认知障碍患者为研究对象, 根据随机数字表分为对照组(n=35)和干预组(n=35)。对照组给予常规治疗和护理, 干预组在对照组基础上施以中医传统功法八段锦联合远隔缺血适应治疗。比较两组干预前、干预12周后的认知功能、日常生活能力和生活质量。结果 干预12周后, 两组蒙特利尔认知量表(MoCA)评分、改良巴氏指数(MBI)评分和脑卒中专用生活质量量表(SS-QOL)评分均较干预前上升(P＜0. 01), 且干预组评分高于对照组, 差异有统计学意义(P＜0. 01)。结论 中医传统功法联合远隔缺血适应治疗对脑卒中后轻度认知障碍患者安全有效, 能够改善整体认知功能, 提高日常生活能力及生活质量。)
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".