The silent occurrence of cerebral small vessel disease in nonelderly patients with type 2 diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: The prevalence of cerebral small vessel disease (SVD) increases in elderly patients with type 2 diabetes (T2DM), exacerbating cognitive decline. However, the prevalence and the severity of SVD in dementia-free nonelderly T2DM patients were largely unknown. Our primary aim is to investigate SVD in such patients, with a specific focus on the correlation between SVD and diabetic peripheral sensorimotor polyneuropathy (DSP). METHODS: We recruited 180 young and middle-aged subjects without cognitive impairment (106 with T2DM, 74 controls). Signs of cerebral SVD on magnetic resonance image were investigated, and the overall SVD burden was evaluated by a combined score. Patients with T2DM underwent further detailed DSP assessment. Regression models were used to investigate the association of SVD with the presence of T2DM, and the associations of the prevalence and severity of SVD and DSP were also explored in patients with T2DM. RESULTS: The prevalence of microbleeds and overall burden of SVD were significantly higher in T2DM patients than in the controls. Further, the presence of DSP related to an increased risk of SVD after adjustment in diabetic group. Moreover, Toronto Clinical Scoring System values were positively associated with the increased SVD scores, and bilateral sural sensory nerve conduction velocities were negatively associated with increasingly severity of SVD scores. CONCLUSION: The current findings extended the increasing prevalence of SVD to dementia-free nonelderly patients with T2DM, suggesting that the time for cognitive screening and prevention might be moved forward in T2DM patients, especially for those with DSP.
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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".