Cognitive Risk Stratification Score in Middle-aged and Older Adults With Type 2 Diabetes: A Cross-Sectional Study
Bibliographic record
Abstract
CONTEXT: Cognitive impairment (CI) affects approximately 45% of middle-aged and older adults with type 2 diabetes mellitus (T2DM) globally. Although formal comprehensive neuropsychological tests are the gold standard for diagnosing CI, they are often time-intensive and may not be feasible in primary care. OBJECTIVE: This study aimed to develop and validate a novel risk stratification score (RSS) to rapidly and comprehensively predict CI risk among middle-aged and older adults with T2DM, offering a streamlined alternative in clinical practice. METHODS: A cross-sectional study was conducted from July 2023 to February 2024 in a primary care polyclinic in Singapore's western region. Participants aged between 40 and 85 diagnosed with T2DM (n = 150) were included in a convenience sampling. The primary outcome was CI status, which was assessed using formal neuropsychological tests, including the Montreal Cognitive Assessment (MoCA). RESULTS: CI was identified in 49.3% of participants (n = 74). The RSS, incorporating the MoCA, diastolic blood pressure, and Short Physical Performance Battery, demonstrated excellent discrimination, achieving an area under the receiver operating characteristic curve of 0.802 (P < .001). With an optimal cutoff of 0.3, the model showed a sensitivity of 63.5% and specificity of 86.8%, effectively differentiating high- and low-risk CI groups. CONCLUSION: RSS in clinical practice, exemplified by the Integrated Metabolic Cognitive Risk Stratification Pathway, is a promising tool for rapid CI risk assessment in primary care. Its robust predictive accuracy and ease of use support its application for early intervention in middle-aged and older adults with T2DM. Future studies should validate its use longitudinally and across diverse populations to enhance generalizability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".