Development and validation of a nomogram for identifying cognitive impairment in patients with leukoaraiosis
Bibliographic record
Abstract
BACKGROUND: Leukoaraiosis (LA) is a common cerebral small vessel disease in elderly populations that frequently leads to cognitive impairment and may progress to vascular dementia. Early identification of cognitive dysfunction remains challenging due to the subtle onset and lack of specific biomarkers. OBJECTIVE: To identify key factors associated with cognitive impairment in LA patients and develop a logistic regression-based identification model to facilitate early clinical recognition and intervention. METHODS: This retrospective cross-sectional study included 390 LA patients admitted to the Department of Neurology between June 2020 and April 2023. Patients were classified into cognitive impairment (CI) and non-cognitive impairment (NCI) groups based on Montreal Cognitive Assessment (MoCA) scores. Data collected included demographics, medical history, biochemical markers, and neuroimaging features. The dataset was randomly split 7:3 into training (n = 273) and validation (n = 117) sets. Univariate analysis identified significant variables (p < 0.05), which were then incorporated into multivariate logistic regression analysis. A nomogram was constructed based on the final model, and performance was evaluated using receiver operating characteristic (ROC) curves and calibration plots for both training and validation sets. RESULTS: In the training set of 273 patients, 137 had cognitive impairment and 136 did not. Univariate analysis revealed that age, Fazekas score, intracranial arterial stenosis assessment (IASA), serum creatinine, and total bilirubin were significantly associated with cognitive impairment (p < 0.05). Multivariate logistic regression identified age (OR = 1.17, 95%CI: 1.11-1.24), IASA (OR = 2.52, 95%CI: 1.64-3.68), and Fazekas score (OR = 2.58, 95%CI: 1.74-3.60) as independently associated factors. The logistic regression model demonstrated excellent discrimination with AUC values of 0.873 and 0.814 for training and validation sets, respectively. Calibration curves showed good agreement between predicted and observed probabilities, confirming model reliability. CONCLUSIONS: Age, intracranial arterial stenosis assessment, and Fazekas score are independently associated with cognitive impairment in LA patients. The logistic regression model with nomogram provides a clinically practical tool for identifying and stratifying patients with cognitive impairment, facilitating targeted clinical assessment and intervention.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".