Cerebral Small Vessel Disease and Cognitive Decline Following Transient Ischemic Attack: A Longitudinal Study
Bibliographic record
Abstract
Abstract Background Cerebral small vessel disease (CSVD) is a common incidental finding on cerebral MRI in patients with transient ischemic attack (TIA) and stroke and has been linked to increased cerebrovascular risk and cognitive decline. This study aimed to investigate the prevalence of CSVD imaging biomarkers in TIA patients and evaluate their association with cognitive function over three years following the ischemic event. Methods A cohort of 246 TIA patients from the INSPiRE-TMS (ClinicalTrials.gov: NCT01586702 ) study were included. The CSVD-score – including white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMBs), and enlarged perivascular spaces (PVS) – was assessed on baseline MRI. Cognitive performance was assessed via the Montreal Cognitive Assessment (MoCA) at baseline and annual outpatient visits up to 3 years. Results CSVD was present in 58.5% of TIA patients. The most prevalent imaging biomarker was lacunes (36.6%), followed by PVS (28.1%), WMH (19.5%) and CMBs (17.9%). Cumulative CSVD-score (range 0-4) was an independently associated with cognitive decline up to 3 years (β = -0.53, 95% CI -0.97 – -0.09, p = 0.018), alongside advanced age (β = -0.08, 95% CI -0.13 – -0.03, p=0.001). CMB burden was the strongest predictive component of the CSVD-score (β = 0.42, 95% CI -0.63 – -0.21, p < 0.001). Specifically, CSVD-score had a significant negative effect on the memory domain of cognitive function with an adjusted β of - 0.18 (95% CI -0.32 – -0.04, p = 0.014). Conclusion Imaging biomarkers of CSVD are present in more than half of TIA patients and are an independent predictor of cognitive decline up to 3 years, with the strongest effect on the memory domain of cognitive function. Whether the presence of CMBs is the strongest predictive imaging biomarker of cognitive decline in TIA patients requires confirmation in further studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".