Bibliographic record
Abstract
Objectives: The purpose of this study was to identify the effectiveness of Korean medicine treatment in mild cognitive impairment (MCI) among a group of community dwelling elderly. Methods: Two-hundred and twenty-nine elderly living in a community and diagnosed with MCI were recruited. Participants were evaluated with various instruments such as the Korean version of Mini- Mental State Examination for Dementia Screening (MMSE-DS) and the Korean version of Montreal Cognitive Assessment (MoCA-K). Korean medicine treatment consisted of herbal medicine, acupuncture, and pharmacoacupuncture. The change in cognitive ability was assessed by using the MMSE-DS and the MoCA-K. Data were analyzed by SPSS/WIN 22.0 using the paired t-test, and the ANOVA. Results: The MMSE-DS and the MoCA-K score generally increased after six months of Korean medicine treatment and the differences in both instruments were statistically significant. Additionally, some consecutive participants maintained long-term cognitive improvement. When analyzed specifically by herbal medicine group based on syndrome differentiation and pharmacoacupuncture group, most showed improvement in the MMSE-DS and the MoCA-K but not all data were statistically significant. The satisfaction score was mostly high and most participants were willing to re-participate in the program. Conclusions: Korean medicine treatment may contribute to the improvement and prevention of cognitive decline in the elderly. However, further systematic research based on large scale sample data and standardized protocols is needed to uplift the welfare and mental health of the elderly.
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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) | 0.000 | 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) | 0.002 | 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".