Imaging and Baseline Predictors of Cognitive Performance in Minor Ischemic Stroke and Patients With Transient Ischemic Attack at 90 Days
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Few studies have examined predictors of cognitive impairment after minor ischemic stroke and transient ischemic attack (TIA). We examined clinical and imaging features associated with worse cognitive performance at 90 days. METHODS: TIA or patients with minor stroke underwent neuropsychological testing 90 days post event. Z scores were calculated for cognitive tests, and then grouped into domains of executive function (EF), psychomotor processing speed (PS), and memory. White matter hyperintensity and diffusion-weighted imaging volumes were measured on baseline magnetic resonance imaging. Ninety-day outcomes included modified Rankin Scale (mRS) and Centre for Epidemiological Studies Depression Scale (CES-D) score. RESULTS: Ninety-two patients were included, 76% male, 54% TIA, and mean age 65.1±12.0. Sixty-four percent were diffusion-weighted imaging positive. Median domain z scores were not significantly different from published norms (P>0.05): memory -0.03, EF -0.12, and PS -0.05. Patient performance ≥1 SD below normal was 20% on memory, 16% on PS, and 17% on EF. Cognitive scores did not differ by diagnosis (stroke versus TIA), stroke pathogenesis, presence of obstructive sleep apnea, and diffusion-weighted imaging or white matter hyperintensity volumes. In multivariable analyses, lower EF was associated with previous cortical infarct on magnetic resonance imaging (P=0.03), mRS score of >1; P=0.0003 and depressive symptoms (CES-D ≥16; P=0.03). Lower PS scores were associated with previous cortical infarct (P=0.02), acute bilateral positive diffusion-weighted imaging (P=0.02), mRS score of >1 (P=0.003), and CES-D ≥16 (P=0.03). CONCLUSIONS: Despite average-range cognitive performance in this TIA and population with minor stroke, we found associations of EF and PS with evidence of previous stroke, postevent disability, and depression.
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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 | 0.004 |
| 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".