Influence of Baduanjin on mild cognitive impairment in elderly diabetic patients
Bibliographic record
Abstract
Objective To explore the influence of Chinese Qigong--Baduanjin on mild cognitive impairment (MCI) in elderly diabetic patients. Methods A total of 78 elderly type 2 diabetic patients complicated with MCI were divided into intervention group and control group by random digits table method. The control group with 41 patients was received routine treatment and discharged guidance after discharging from hospital. The intervention group with 37 patients was received Baduanjin exercise for 12 months based on the routine treatment in control group. Montreal Cognitive Assessment (MoCA) and Activity of Daily Living (ADL) scores in two groups were compared before intervention and 1, 3, 6, 9 and 12 months after intervention. Results In intervention group, MoCA scores were significantly increased at different times after intervention compared with those before intervention, F=72.782, the differences were statistically significant, P<0.05. At the same time, MoCA scores were all significantly higher in intervention group than those in control group, F=2.231, 2.972, 4.362, 5.085, 5.373, the differences were statistically significant, P<0.05. In intervention group, ADL scores had significantly improved since 6 months after intervention compared with those before intervention, F=93.126, the differences were statistically significant, P<0.05, and compared with those in control group, the scores were significantly higher since 6 months after intervention, F=3.853, 4.561, 7.162, the differences were statistically significant, P<0.05. Conclusion Chinese Qigong--Baduanjin in treatment of elderly diabetic patients complicated with MCI can delay the MCI progress and improve ADL of the patients. Key words: Diabetes mellitus, type 2; Cognitive impairment; Baduanjin
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".