Functional, cognitive and physical outcomes 3 years after minor lacunar or cortical ischaemic stroke
Bibliographic record
Abstract
OBJECTIVE: Many studies examining stroke outcomes focus on more severe strokes or have short follow-up periods, so the long-term outcomes post-minor ischaemic stroke are unclear. METHODS: We recruited participants from inpatient and outpatient services with a lacunar or minor cortical ischaemic stroke (National Institutes of Health Stroke Scale score <8) and assessed current and premorbid cognitive functioning (Addenbrooke's Cognitive Examination-Revised (ACE-R), National Adult Reading Test (NART)), physical functioning (Timed Get Up and Go (TUG), 9-Hole Peg Test (9HPT)), dependency (modified Rankin Scale (mRS)), depression (Beck's Depression Inventory) in-person and remotely (Stroke Impact Scale). RESULTS: We followed up 224/264 participants at 3 years (mean age at index stroke=67, 126 (56%) men, 25 non-contactable, 15 declined): 66/151 (44%) had cognitive impairment, mean ACE-R 88 (SD 9, range 54-100/100), 61/156 (39%) had depression and 26/223 (12%) were dependent (mRS=3-5). Cognitive impairment at 3 years affected all ACE-R subdomains and was associated with ACE-R 1 year (β=1.054, p<0.001) and NART (β=1.023, p<0.05). Poor physical function was associated with stroke severity (TUG, β=1.064, p<0.01) and recurrent stroke (9HPT, β=1.130, p<0.05 right, β=1.214, p<0.05 left). Higher ACE-R scores were associated with faster TUG (β=-0.279, p<0.05) and 9HPT (right β=-0.257, p<0.05; left β=-0.302, p=0.05) and inversely with dependency (mRS=3-5, OR 0.88, 95% CI 0.80 to 0.97). We adjusted analyses for demographic, stroke and known risk factors. In-person and remote assessments were highly correlated. CONCLUSIONS: Cognitive, physical impairments and depression are common and interrelated 3 years after minor stroke. Cognitive and physical impairments require rehabilitation after minor stroke and argue for better integration of stroke and dementia services.
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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) | 0.001 | 0.000 |
| 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".