The impact of early-life intelligence quotient on post stroke cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment can complicate minor stroke, but there is limited information on risk factors including peak cognitive ability earlier in life. METHODS: We recruited patients with clinically-evident lacunar or minor non-lacunar ischaemic stroke, recorded clinical features, vascular risk factors, magnetic resonance imaging-detected stroke sub-type and small vessel disease burden. At 1-3 and 12 months after stroke, we assessed educational attainment (years of education), current cognition (Addenbrooke's Cognitive Examination-Revised), pre-morbid intelligence (National Adult Reading Test) and dependency (modified Rankin Scale). RESULTS: We recruited 157 patients (87 lacunar, 64 non-lacunar ischaemic strokes), median age 66 (inter-quartile range 56-74) years, 36/157 (23%) patients had a Addenbrooke's Cognitive Examination-Revised score < 82 at one to three months, 29/151 (19%) had a Addenbrooke's Cognitive Examination-Revised < 82 at one year. Lower National Adult Reading Test score (cognitive impairment per point on National Adult Reading Test odds ratio 0.91, 95% confidence interval 0.87, 0.95) and older age (per year of age odds ratio 1.04 (95% confidence interval 1.01, 1.08) predicted one-year cognitive impairment more than stroke severity (per point on National Institute of Health Stroke Scale odds ratio 0.96 (95% confidence interval 0.0.68, 1.31)) or vascular risk factors e.g. hypertension (odds ratio for diagnosis of hypertension 0.52 (95% confidence interval 0.24, 1.15). Cognitive impairment was associated with having more white matter hyper-intensities (odds ratio per point increase in Fazekas score 1.42, 95% confidence interval 1.11, 1.83). DISCUSSION: This observational study provides evidence that pre-morbid intelligence quotient and education predict cognition after stroke, and confirms the association between cognitive impairment and small vessel disease. CONCLUSION: Pre-morbid intelligence should be considered in future studies of post-stroke cognition.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".