Diagnosis of TCM symptoms and analysis of risk factors of mild cognitive impairment in patients with type 2 diabetes mellitus.
Bibliographic record
Abstract
OBJECTIVE: To explore the diagnosis of traditional Chinese medicine (TCM) syndrome and analyze the risk factors of mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM). METHODS: 141 T2DM patients, who were hospitalized in department of endocrinology of our hospital from February 2020 to December 2020, were chosen as research subjects. The patients were divided into an observation group (n=65, T2DM with MCI) and a control group (n=76, T2DM with normal cognitive function) according to the Montreal Cognitive Assessment (MoCA) score and diagnostic criteria of MCI. Pearson correlation analysis was used to study the correlation between MoCA score and influencing factors, and multiple logistic regression analyses were applied to analyze the risk factors of T2DM patients. RESULTS: =0.0000). According to the results of multivariate logistic regression analysis, age, course of diabetes, homocysteine (HCY) and glycosylated hemoglobin (HbAlc) were the independent risk factors of MCI, and the education level was a protective factor. CONCLUSION: Mental deficiency and phlegm obstruction are common in T2DM patients complicated with MCI. The factors such as age, diabetes course, education degree, HCY and HbAlc are closely related to MCI. The occurrence of MCI in T2DM patients can be prevented by improving the education degree of patients, effective control of blood glucose and reduction of HCY level.
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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 | 0.001 | 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".