136Rates, risks and routes to reduce vascular dementia (R4VAD)
Bibliographic record
Abstract
Introduction: Stroke is common in older adults and increases the risk of cognitive impairment and vascular dementia. However, there is a lack of knowledge about risk factors which restricts mechanistic understanding, prevention, treatment and design of patient services. R4VAD is a multi-site longitudinal, inclusive study in patients presenting with stroke to the UK Stroke Centres. The aim of the study is to determine rates of, and risk factors for, cognitive and related impairments after stroke to assess mechanisms and improve prediction models. Methods: We will recruit ~2000 patients within 6 weeks of stroke and collect patient, stroke, socioeconomic, lifestyle, cognitive, fatigue, mood and informant data appropriate to the stroke stage. More detailed assessments will be obtained at 6+/−2 weeks post-baseline assessment and annual follow-up will be conducted by phone and post to at least 2 years. We will assess diagnostic neuroimaging (MR and CT) in all patients, and high-sensitivity inflammatory blood markers and genetic analysis in as many patients as possible. Participants will be in follow-up and consented for re-contact, facilitating future clinical trials. Results: The study has been reviewed by ethics and the protocol is in the final stages of development with site identification underway. Conclusion: R4VAD will provide reliable data on cognition long-term after stroke and will improve understanding of clinical, demographic, laboratory, neuroimaging and social predictors of post-stroke cognitive impairment and vascular dementia. This will improve risk stratification, identification of mechanisms and intervention targets.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".