[Screening and evaluation of clinical predictors of type 2 diabetes mellitus with cognitive impairment].
Bibliographic record
Abstract
The present study aims to screen and evaluate the early clinical predictors for type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI) and dementia in Hunan province. A cross-sectional study was conducted from May 2023 to October 2023 to collect data on long-term T2DM patients who settled in Hunan province and were treated in the Department of Geriatrology at Xiangya Hospital of Central South University. The patients were grouped according to the Montreal Cognitive Assessment (MoCA) scale. Basic patient information and multiple serum markers were collected, and differences between groups were compared using one-way ANOVA or Kruskal-Wallis (KW) tests. The multivariate logistic regression analysis was utilized to assess risk factors and Nomogram models were constructed. The logistic regression analysis showed that years of education and serum levels of 1, 5-AG were related factors for the progression of T2DM to T2DM with MCI, and body weight, years of education and FPN levels affected the progression of T2DM with MCI to T2DM with dementia. Based on this, two Nomogram risk prediction models were established. The area under the curve (AUC) of the Nomogram model predicting T2DM progression to T2DM combined with MCI was 0.741, and the AUC of the Nomogram model predicting T2DM combined with MCI progression to T2DM combined with dementia was 0.734. The calibration curves (DCA) of the two models in the training and validation sets were symmetrically distributed near the diagonal line, indicating that the models in the training and validation sets could match each other. In summary, body weight, years of education, and serum HDL-3, FPN, and 1, 5-AG levels are associated with the development of MCI and dementia in T2DM patients. The Nomogram models constructed based on these factors can predict the risk of MCI and dementia in T2DM patients, providing a basis for clinical decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".