Association of white matter hyperintensity with systemic inflammation markers and cognitive assessments: a cross-sectional study via SHAP analysis
Bibliographic record
Abstract
Background: White matter hyperintensity (WMH), a common neuroimaging feature in the older adults, has not been systematically elucidated regarding its association with cognitive function and systemic inflammation. Aim: To develop and validate a clinical model for higher WMH burden integrating MoCA and CBC-derived inflammatory markers, and to quantify their independent and joint associations with WMH severity. Methods: This study retrospectively collected data from patients with WMH at the First Affiliated Hospital of Baotou Medical College (December 2023-December 2024). We used univariate and multivariate logistic regression analyses to identify WMH-related variables. Then, we constructed an artificial neural network model and performed 10-fold cross-validation for internal validation and model performance comparison. The Shapley Additive Explanations (SHAP) method was employed to evaluate both models. Results: < 0.01). Multivariate logistic regression analysis identified age, hypertension, high-density lipoprotein (HDL), previous cerebrovascular disease, the systemic inflammation response index (SIRI), and the Montreal Cognitive Assessment (MoCA) score as independent predictors of WMH burden. Ten-fold cross-validation showed that the set neural network model performed as well as the logistic regression model (AUC = 0.81). SHAP-based visual analysis identified age, MoCA score, and hypertension as key driving factors. Conclusion: Age, hypertension, previous cerebrovascular disease, HDL, SIRI, and MoCA score are independent risk factors for moderate to severe WMH occurred. The model integrating MoCA and inflammatory markers accurately predicts moderate to Severe WMH. This study offers a multidimensional assessment framework for WMH risk stratification and early 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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".