White Matter Free Water Outperforms Cerebral Small Vessel Disease Total Score in Predicting Cognitive Decline in Persons with Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Vascular pathology is an important partner of Alzheimer's disease (AD). Both total cerebral small vessel disease (CSVD) score and white matter free water (FW) are useful markers that could reflect cerebral vascular injury. OBJECTIVE: We aim to investigate the efficacy of these two metrics in predicting cognitive declines in patients with mild cognitive impairment (MCI). METHODS: We enrolled 126 MCI subjects with 3D T1-weighted images, fluid-attenuated inversion recovery images, T2* images, diffusion tensor imaging images, cerebrospinal fluid biomarkers and neuropsychological tests from the Alzheimer's Disease Neuroimaging Initiative database. The total CSVD score and FW values were calculated. Simple and multiple linear regression analyses were applied to explore the association between vascular and cognitive impairments. Linear mixed effect models were constructed to investigate the efficacy of total CSVD score and FW on predicting cognitive decline. RESULTS: FW was associated with baseline cognition and could predict the decline of executive and language functions in MCI subjects, while no association was found between total CSVD score and cognitive declines. CONCLUSION: FW is a promising imaging marker for investigating the effect of CSVD on AD progression.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".