White matter hyperintensities and mild behavioral impairment: Findings from the MEMENTO cohort study
Bibliographic record
Abstract
Background: White matter hyperintensities (WMH) contribute to cognitive decline and increase risk for dementia. Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by the emergence and persistence of neuropsychiatric symptoms (NPS) in later life as an at-risk state for incident cognitive decline and dementia. Both WMH and MBI are common in patients with mild cognitive impairment (MCI), but few studies have established the link between these two risk markers in this population. Methods: Participants were memory clinic patients with MCI from the French MEMENTO study. WMH volume was quantified using brain magnetic resonance imaging. Participants were categorized into MBI+ and MBI- status based on NPS persistence, and the association between MBI status and domains with WMH volume was assessed with linear regression. Results: = 0.01 (95% CI 2.0% to 16.7%)]. In this model, MMSE score was not associated with WMH volume. None of the MBI domains individually predicted greater WMH volume, although emotional dysregulation, impulse dyscontrol, and apathy trended towards significance. Conclusions: In a memory clinic sample of older adults with MCI, MBI was associated with higher WMH volume. Global MBI status outperformed MMSE and individual MBI domains, supporting the utility of MBI, a multi-NPS-domain composite assessment, for predicting WMH volume.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".