13 Neuroimaging correlates of vascular cognitive impairment: prevalence and clinical relevance of mesial temporal lobe atrophy in a stroke service cohort
Bibliographic record
Abstract
Objectives Vascular cognitive impairment and Alzheimer9s disease (AD) may interact and co-exist. We investigated the prevalence and impact of mesial temporal lobe atrophy (MTA), a radiological marker for AD, in a population of stroke patients. Methods Patients referred to a stroke service underwent detailed neuropsychological testing and standardised imaging, including GRE T2* MRI and FLAIR. We assessed MTA on FLAIR images using a newly developed and validated visual rating scale based on the Scheltens scale (scores 0 to 4; mild MTA <2 and severe ≥2). Microbleeds and white matter changes (WMC) were rated using validated scales. The effect of MTA on cognitive functions was tested using multivariate regression analyses. Findings 396 patients with full neuropsychological testing and complete MRI sequences were included (358 mild and 38 severe MTA). 171 patients (43%) showed some degree (score ≥1) of MTA. Patients with severe MTA were older (76 vs 64, p=0.000), more hypertensive (97% vs 66%, p=0.000), and had more severe WMC (median 9 vs 5.5, p=0.000) than patients with mild or no MTA. In adjusted multivariate analyses, MTA was a predictor of verbal memory impairment (OR 1.81, p=0.004) and frontal executive dysfunction (OR 1.48, p=0.023). Conclusions MTA is common in stroke patients and is independently associated with verbal memory and frontal executive deficits. Alzheimer pathology may play an important role in cognitive impairment in patients with cerebrovascular disease.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".