Cortical cerebral microinfarcts predict cognitive decline in memory clinic patients
Bibliographic record
Abstract
Cortical cerebral microinfarcts (CMIs) - a novel MRI marker of cerebral vascular pathology have been linked with dementia and impaired cognition in cross-sectional studies. However, it is unknown if cortical CMIs are an indicator of further cognitive decline. We sought to examine whether baseline cortical CMIs predict cognitive decline in a prospective memory-clinic setting. A total of 313 patients with baseline 3T MRI scans and at least two neuropsychological assessments obtained a minimum of one year apart were recruited. Cortical CMIs were graded on baseline MRI according to a validated protocol. The Montreal Cognitive Assessment (MoCA) and a detailed neuropsychological battery were used to assess cognition. Patients with increased cortical CMIs showed greater decline in MoCA and global cognition per year. Patients with > 2 cortical CMIs decline on average by 2 scores on MoCA and 0.5 on global cognition at year two which corresponds to 109.8% and 184.5% greater decline when compared to those without CMIs. Furthermore, cortical CMIs at baseline were associated with accelerated decline in memory and language domains. Similar associations were observed when analysis was restricted to demented patients. Cortical CMIs together with other cerebrovascular disease markers can be used to design clinical trials in vascular cognitive impairment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".