Effect of electroacupuncture at the acupoints for <i>Tiaozang Xingshen</i> on cerebral metabolism in the patients with type 2 diabetes mellitus-associated cognitive dysfunction.
Bibliographic record
Abstract
OBJECTIVES: function and rescuing the spirit) in treatment of T2DACD, using magnetic resonance spectroscopy. METHODS: at Baihui (GV 20) and Shenting (GV 24), and bilateral Feishu (BL 13), Pishu (BL 20), Shenshu (BL 23), Zusanli (ST 36), Sanyinjiao (SP 6), Hegu (LI 4) and Taichong (LR 3). EA was operated with disperse-dense wave, 2 Hz/100 Hz in frequency and 0.1 mA to 1.0 mA in current intensity; 30 min each time, once daily. One course of EA consisted of 5 treatments, at the interval of 2 days and the intervention lasted 8 courses. Before treatment in the control group, before and after treatment in the observation group, the score of Montreal cognitive assessment scale (MoCA), the score of clinical dementia rating (CDR), Flanker paradigm, Stroop paradigm, Nback paradigm, the score of self-rating anxiety scale (SAS), the score of self-rating depression scale (SDS), and the score of Hamilton depression rating scale (HAMD) were evaluated separately; the glycolipid metabolic indexes (fasting plasma glucose [FPG], glycosylated hemoglobin type A1c [HbA1c], total cholesterol [TC], triacylglycerol [TG], high-density lipoprotein cholesterol [HDL-C] and low-density lipoprotein cholesterol [LDL-C]) were determined；with the magnetic resonance spectroscopy technique adopted, the metabolites in the basal ganglia area were detected. The correlation analysis was performed for the metabolite values with MoCA score, CDR score , Flanker paradigm, Stroop paradigm, and Nback paradigm. RESULTS: 0.039). CONCLUSIONS: function and rescuing the mind" improves the cognitive function of T2DACD patients, which may be related to the regulation of NAA, MI and GABA levels in the basal ganglia.
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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) | 0.000 | 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) | 0.001 | 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".