P4‐147: Deep subcortical white matter disease is a major risk factor for late post stroke cognitive impairment
Bibliographic record
Abstract
Prevalence of post stroke cognitive impairment (PSCI) has been shown to be variable at different intervals post stroke. Identifying factors that predict longitudinal cognitive performance will influence management of stroke patients. We aimed to study the influence of pre-stroke cerebral white matter disease on early and late cognitive outcomes. Prospective cohort study of stroke patients attending a tertiary neurology center. Patients with MRI confirmed acute small vessel infarcts without pre-existing dementia were recruited. Logistic regression analysis was used to determine the association between MRI white matter hyperintensity and cognitive performance. 145 patients with a mean age of 55.8 years were longitudinally studied. Cognitive evaluation was performed at month 3 and month 12 post stroke. At month 12, 32.9% of subjects had poor MOCA scores. PSCI patients were older (62.3 vs. 55.5; p = 0.009) and had lower years of education (10.3 vs. 8.3 years; p = 0.009). There was no significant difference in the prevalence of diabetes mellitus, hypertension or hypercholesterolemia. The mean MOCA score at month 3 and 12 among PSCI patients were 21.7 and 22.9 respectively. Depression scores were not significantly different between the two groups. Patients with PSCI had significantly greater pre-stroke white matter hyperintensity (WMH). The deep subcortical white matter hyperintensity was significantly greater in the PSCI group (2.15 vs. 0.85; p = < 0.001) while the periventricular white matter hyperintensity demonstrated a trend towards significance (2.35 vs. 1.39; p = 0.067). Left sided deep subcortical white matter hyperintensity was associated with a 3.5 times increased risk of PSCI. White matter hyperintensity, specifically deep subcortical white matter hyperintensity is an important risk factor for late PSCI.
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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 | 0.000 | 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) | 0.009 | 0.001 |
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".