Effectiveness of Traditional Chinese Medicine (TCM) treatments on the cognitive functioning of elderly persons with mild cognitive impairment associated with white matter lesions.
Bibliographic record
Abstract
BACKGROUND: Cerebral white matter lesion (WML) is a pathological change of the white matter which is considered an early sign of brain impairment in elderly individuals, so it is reasonable to administer early dementia prevention programs to individuals with WML.Traditional Chinese Medicine (TCM) has developed several approaches to prevent or delay the onset of dementia that have, as yet, not been formally tested. AIM: Evaluate the effects of a 6-month TCM intervention for elderly persons with mild cognitive impairment and WML. METHODS: Eighty individuals 65 years of age or older with radiological evidence of WML and mild cognitive impairment based on the Montreal Cognitive Assessment (MoCA) were classified into the four main TCM constitutional types (qi deficiency, yang deficiency, phlegm dampness, or blood stasis) and randomly assigned to a treatment group or a treatment-as-usual control group. The treatment group participated in training focused on diet, lifestyle, exercises, and emotional regulation adjustment; they also received six monthly courses of moxibustion (heating acupoints by burning the moxa of dried mugwort), each of which involved 10 daily 15-minute sessions focused on three targeted acupoints (one of which was specific to the constitutional type). Changes in the MoCA and in the score of each of the four constitutional types were the main outcomes assessed. RESULTS: Two participants dropped out of each group over the 6 months, leaving 38 in each group. Based on repeated measures analysis of variance, the total MoCA score, four of the six MoCA subscales scores (visual space and executive function, naming, attention and calculation, and delayed memory), and all four of the TCM constitution type scores showed significantly greater improvement over the 6 months in the treatment group than in the control group. CONCLUSION: This study shows that TCM interventions can improve both the cognitive functioning and the severity of symptoms considered in the TCM assessment of constitutional types among elderly individuals with mild cognitive impairment and WML. Long-term follow-up studies that use blinded evaluation of the outcome are needed to determine whether or not constitution-specific TCM treatments can prevent the onset of dementia.
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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 | 0.001 |
| 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".