Post-stroke Cognitive Impairment—Impact of Follow-Up Time and Stroke Subtype on Severity and Cognitive Profile: The Nor-COAST Study
Bibliographic record
Abstract
BACKGROUND: Post-stroke cognitive impairment (PSCI) is common, but evidence of cognitive symptom profiles, disease course over time, and pathogenesis is scarce. We investigated whether time and etiologic stroke subtype were of importance for the probability for PSCI and severity and cognitive profile. METHODS: Stroke survivors (n=617) underwent cognitive assessments of attention, executive function, memory, language, perceptual-motor function and administered the Montreal Cognitive Assessment (MoCA) after 3 and/or 18 months. PSCI was classified according to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria. Stroke severity was assessed with the National Institutes of Health Stroke Scale (NIHSS) at admittance and stroke subtype was categorized as intracerebral hemorrhage (ICH), large artery disease (LAD), cardioembolic stroke (CE), small vessel disease (SVD), un-/other determined strokes (UD). Mixed-effects logistic or linear regression was applied, with PSCI, MoCA, and z-scores of the cognitive domains as dependent variables. Independent variables were time as well as stroke subtype, time, and interaction between these. The analyses were adjusted for age, education, and sex. RESULTS: Mean age was 72 years (SD 12), 42 % were females, and mean NIHSS score at admittance was 3.8 (SD 4.8). Probability for PSCI after 3 and 18 months was 0.59 (95%CI 0.51-0.66) and 0.51 (95%CI 0.52-0.60) respectively and did not change over time. Global measures and almost all cognitive domains were impaired for the entire stroke population and for almost all stroke subtypes. Executive function and language improved for the entire stroke population, and after dividing the sample according to stroke subtypes, language improved for ICH patients. No significant differences were found in the severity of impairment between stroke subtypes, except for attention which was impaired for LAD and CE in contrast to no impairment for SVD. CONCLUSIONS: PSCI is common for all stroke subtypes, with impairment in several cognitive domains noted early after a stroke as well as a long time after a stroke. Increased evidence of symptom profile might be important for personalizing rehabilitation, while stroke subtypes may offer new insight into underlying mechanisms. Further research is needed on underlying mechanisms, prevention and treatment of PSCI, and on relevance for rehabilitation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".