Increased regional white matter hyperintensity volume in objectively-defined subtle cognitive decline and mild cognitive impairment
Bibliographic record
Abstract
White matter hyperintensities (WMH), a marker of small vessel cerebrovascular disease, increase risk of developing mild cognitive impairment (MCI) and Alzheimer's disease (AD). Less is known about the extent and pattern of WMH in pre-MCI stages, such as among those with objectively-defined subtle cognitive decline (Obj-SCD). Five hundred and fifty-nine Alzheimer's Disease Neuroimaging Initiative participants (170 cognitively unimpaired [CU]; 83 Obj-SCD; 306 MCI) free of clinical dementia or stroke completed neuropsychological testing and MRI exams. ANCOVA models compared cognitive groups on regional WMH adjusting for age, sex, and apolipoprotein E (APOE) ɛ4 frequency. Compared with the CU group, those with Obj-SCD had greater temporal, occipital, and frontal WMH whereas those with MCI had higher WMH volume across all regions (p's < 0.01). No differences in WMH volume were observed between the Obj-SCD and MCI groups (p's > 0.05). Findings add to growing evidence of associations between Obj-SCD and imaging biomarkers, providing support for utility of these criteria to capture subtle cognitive changes that are biologically based.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".