Clinical study on decreasing occurance of Alzheimer's Disease through intervention on amnestic Mild Cognitive Impairment with Di-Huang-Yi-Zhi formula in Shigatse, Tibet
Bibliographic record
Abstract
Abstract Background Mild cognitive impairment (MCI) is generally considered a transitional stage between normal aging and AD dementia. This study aimed to analyze the efficacy of Di-Huang-Yi-Zhi (DHYZ) formula in treating amnestic Mild Cognitive Impairment (aMCI) for the patients in high altitude area (Qinghai Tibet Plateau). Method: A total of 106 patients in Shigatse, Tibet were randomly allocated into two groups. One group were to receive DHYZ decoction (150 ml each time, twice a day), the other group were to have aniracetam capsule (200 mg each time, three times a day) ,with 53 patients in each group. Changes in neuropsychological scales including mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), the Barthel Index for activities of daily living (ADL) and TCM symptoms were detected during a 12-month treatment period. After one year follow-up, the conversion rate of AD was observed. Result There was no significant difference between the two groups in baseline characteristics and scale scores (P > 0.05). Compared with the aniracetam group, the DHYZ group showed statistically higher MMSE and MoCA score and lower TCM score at the 9-month and 12-month. In addition, the ADAS-Cog and ADL scores in DHYZ group at 12-month were lower than that in the aniracetam control group. After one year follow-up, the conversion rate of AD in DHYZ group was 10% (5/50), and aniracetam group was 15.69% (8/51). The conversion rate of AD in DHYZ group was significantly lower than that in aniracetam group. Conclusion DHYZ formula can improve the cognition behavior and global function of patients with aMCI, it can also delay the conversion to AD. This is represents a new treatment option for the patients.
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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) | 0.001 | 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".