Korean Traditional Medicine in Treating Patients with Mild Cognitive Impairment: A Multicenter Prospective Observational Case Series
Bibliographic record
Abstract
In Korea, patients with mild cognitive impairment can choose to receive treatment of Korean medicine, and Korean medicine hospitals provide specialized medical care for the prevention and management of cognitive disorders. The aim of the study is to explore the role of Korean medicine therapy for patients with mild cognitive impairment in a real clinical setting. Fifteen patients with amnestic mild cognitive impairment were enrolled in this prospective observational study in three Korean medicine hospitals. Korean medicine treatments were delivered by experienced professionals and not restricted to standardized treatment. Outcome measures were prospectively planned to examine the Korean‐Montreal Cognitive Assessment (K‐MoCA), Korean‐Mini Mental State Examination (K‐MMSE), and other detailed neuropsychological assessment at the baseline and after 12 and 24 weeks of treatment. Korean medicine treatment for MCI treatment in the real‐world clinical setting included herbal medicine and acupuncture. The most frequently used herbs in herbal decoctions were Acori Graminei Rhizoma, Polygalae Radix, and Poria Sclerotium Cum Pini Radix. The herbal medicine formulae used in this study were classified into three categories: tonifying Qi (33.3%), tonifying kidney (46.7%), and calming liver (20%) formulae. In the cognitive ability assessment, the K‐MoCA score significantly improved after treatment (mean difference 2.6; 95% CI: 1.3 to 3.9, p = 0.001). The K‐MMSE score slightly increased after treatment; however, the improvement was not statistically significant (mean difference 0.8; 95% CI: −0.5 to 2.0, p = 0.195). In detailed neuropsychological assessment, the cognitive domains of executive functions and memory after the treatment were distinctively improved. In this prospective observational case series, we could see the real clinical environments of treating patients with mild cognitive impairment in Korean medicine hospitals. Patients treated with Korean medicine showed improved results in the neuropsychological assessment after 12 and 24 weeks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".