Associations of white matter hyperintensities with cognitive decline: A longitudinal study
Bibliographic record
Abstract
Abstract Background White matter hyperintensities (WMHs), mainly caused by cerebrovascular injury, may lead to cognitive impairment. In order to identify whether the volume of WMHs is associated with cognitive decline over years, this longitudinal study involved 818 individuals from the ADNI‐2 dataset from August 2010 to May 2017. Method Cross‐sectional and longitudinal associations of WMHs with 8 cognitive domains were explored, using Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating Sum of Boxes (CDRSB), Alzheimer Disease Assessment Scale‐Cognitive (ADAS‐Cog13), Rey Auditory Verbal Learning Test (RAVLT), Functional Assessment Questionnaire (FAQ), executive function (ADNI‐EF), and memory function (ADNI‐Mem). The association analyses were performed using multiple linear regression models, linear mixed models, Spearman rank correlation and Kaplan‐Meier survival curves. Result The volumes of WMHs were greater in patients with AD dementia compared with controls (p<0.001) and MCI (p=0.006) patients at baseline. The bigger volumes of WMHs correlated with worse performances on ADAS‐Cog13 and ADNI‐EF (p=0.029; p=0.0032) at baseline and MMSE, MoCA, CDRSB, ADAS‐Cog13, FAQ and ADNI‐Mem (overall p<0.05) longitudinally, after adjusting for age, sex, educational level, apolipoprotein E ε4 genotype, hypertension, hyperlipidemia, diabetes, smoking, infarction and diagnosis. And the correlations between the change rate of WMHs and change rates of MMSE, MoCA, CDRSB, FAQ, ADNI‐EF and ADNI‐Mem were statistically significant. Furthermore, patients with high WMH volumes showed an increased likelihood of dementia. Conclusion The results of the study suggest that WMH volume is associated with cognitive decline, and it contributes to the conversion to AD.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".