The relationship of small vessel disease burden on cerebral and regional brain atrophy rates and cognitive performance over one year of follow-up after transient ischemic attack
Bibliographic record
Abstract
Background Stroke, even when minor, increases the risk of dementia. We aimed to determine whether patients with transient ischaemic attack (TIA) exhibit higher rates of cerebral and regional atrophy 1-year after first stroke symptoms and evaluate the relationship with small vessel disease and cognitive performance. Methods TIA patients and controls without cognitive symptoms underwent high-resolution T1-weighted MRI and cognitive testing at baseline and 1-year. Percent brain volume change (PBVC) was measured, and the location of regional atrophy and small vessel disease (CSVD) burden was evaluated. Neuropsychological testing assessed memory, processing speed, and executive function. Results A total of 76 TIA patients and 53 controls of mean age 67 (SD = 8) and 68 years (SD = 8) were recruited. TIA patients demonstrated greater improvement of visual memory and executive function at 1-year. TIA patients had greater median PBVC/year compared to controls (−0.79% [(−1.22)-(−0.38)] vs. -0.41% [(−0.62)-0.19]; p < 0.001), and higher rates of volume loss (ml/year) in subcortical gray (−0.53 [(−1.09)-(−0.06)] vs. -0.13 [(−0.61)-0.31]; p < 0.05) and white matter (−2.21 [−5.47, 0.40] vs. -0.93 [(−3.43)-2.10]; p < 0.05). Linear regression showed that TIA, age, and systolic blood pressure (SBP) were associated with greater cerebral volume loss over 1-year. There was no significant relationship between PBVC and 1-year cognition. Conclusion A near two-fold increase in rate of cerebral atrophy 1-year after TIA is associated with higher SBP emphasizing the need for improved treatment of SBP. Cerebral and regional atrophy rates may be used to select patients for vascular risk reduction trials or novel therapeutics in future dementia prevention trials.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".