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Record W4396586719 · doi:10.1101/2024.04.30.24306637

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)

2024· preprint· en· W4396586719 on OpenAlexaff
Philip M. Bath, Ellen V. Backhouse, Rosalind Brown, Lisa J Woodhouse, Fergus Doubal, Terence J. Quinn, Hugh S. Markus, Richard J. McManus, John T. O’Brien, Thompson Robinson, David J. Werring, Nikola Sprigg, Adrian Parry‐Jones, Rhian M. Touyz, Steven Williams, Yee‐Haur Mah, Hedley Emsley, Joanna M. Wardlaw

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsMcGill University
FundersBritish Heart Foundation
KeywordsDementiaObservational studyBaseline (sea)Stroke (engine)CognitionCohort studyMedicineVascular dementiaCohortProspective cohort studyCognitive impairmentPsychologyPsychiatryInternal medicinePolitical scienceEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.092
GPT teacher head0.338
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreProtocol

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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