Rates, risks and routes to reduce vascular dementia (R4VaD), a UK-wide multicentre prospective observational cohort study of cognition after stroke: baseline data and statistical analysis plan (ISRCTN18274006)
Bibliographic record
Abstract
INTRODUCTION: Stroke is often followed by vascular cognitive impairment and vascular dementia, the most feared complications of stroke. However, understanding of post-stroke cognitive impairment remains limited. METHODS: Rates, risks, and routes to reduce vascular dementia is an observational cohort study of post-stroke cognition. Patients with haemorrhagic or ischaemic stroke, or transient ischaemic attack, were recruited within 6 weeks of stroke from hospitals across the UK. Consent was obtained from patients with capacity or from relatives/friends in those without capacity. The primary outcome is dementia rate and its severity at 2 years assessed using a 7-level ordinal cognition outcome. Final dementia rate will be compared in those with mild stroke/TIA (worst NIHSS ≤7) versus severe stroke (NIHSS >7). Secondary outcomes will include cognitive impairment, function, mood and quality of life, and predictors of cognitive impairment at 1 and 2 years. Data are shown as number (%), median (interquartile range [IQR]), or mean (standard deviation [SD]). RESULTS: We recruited 2,441 patients from 50 hospitals. Of these, 2,432 (99.6%) had a qualifying event of stroke or TIA. The mean age was 68.2 years (SD 13.5), females 979 (40.3%), ethnic minority 170 (7.0%), NIHSS ≤7.1962 (80.7%), onset to recruitment 5 days (IQR 3-13) and diagnosis ICH 192 (7.9%), ischaemic stroke 2,097 (86.2%), TIA 143 (5.9%). The distribution of cognition at baseline (within 6 weeks of onset) was normal 1,300 (53.5%), minor neurocognitive disorder - single domain 351 (14.4%), minor neurocognitive disorder - multi-domain 263 (10.8%), major neurocognitive disorder - mild 387 (15.9%), major neurocognitive disorder - moderate 108 (4.4%), and major neurocognitive disorder - severe 23 (0.1%). Participants with more severe stroke were recruited later (8 [IQR 3-17] vs. 5 [2-11] days, p < 0.001), less likely to have capacity (86.3% vs. 96.8%, p < 0.001) and more likely to have had an intracerebral haemorrhage (12.0% vs. 6.8%, p < 0.001). CONCLUSION: We provide baseline data with the statistical analysis plan in the supplementary material. The data highlight the substantial under-appreciated cognitive burden of stroke, even in the first few days and weeks after onset.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".