Acupuncture effect on the magnetic resonance imaging (MRI) test for mild cognitive impairment leukoaraiosis
Bibliographic record
Abstract
Vascular cognitive impairment refers to the risk factors derived from blood vessels caused by a variety of levels and types of cognitive impairment, such as vascular mild cognitive impairment, vascular dementia and mixed dementia. Using acupuncture for mild cognitive impairment leukoaraiosis has impacts on the functional magnetic resonance. To know the neurotransmitter changes before and after acupuncture intervention in mild cognitive impairment with the functional magnetic resonance brain, laboratory for music and communication in infancy (LAMCI) cases (23), and 13 normal subjects (control group) cases were observed. Acupuncture treatment and the natural process group were observed for 3 months, respectively. Neuropsychological evaluation and magnetic resonance spectroscopy check were executed at the beginning and end of each day. Neuropsychological assessment includes mini-mental state examination (MMSE) and montreal cognitive assessment (MoCA), magnetic resonance imaging (MRI), spectroscopy N-acetylaspartate (NAA)/creatine (Cr), choline (Cho)/Cr, and myoinositol (MI)/Cr. The results showed that acupuncture can be used as an effective means of intervention for mild cognitive impairment leukoaraiosis, which can reduce activated brain regions in order to raise the efficiency of the task when stimulation is processed. Key words: Acupuncture, leukoaraiosis, mild cognitive impairment, functional magnetic resonance, magnetic resonance spectroscopy.
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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".