Cerebral Small Vessel Disease, Risk Factors, and Cognition in Tenants of Precarious Housing
Bibliographic record
Abstract
Background and Purpose: We aim to describe the burden, characteristics, and cognitive associations of cerebral small vessel disease in a Canadian sample living with multimorbidity in precarious housing. Methods: Participants received T1, T2-fluid-attenuated inversion recovery, and susceptibility-weighted imaging 3T magnetic resonance imaging sequences and comprehensive clinical, laboratory, and cognitive assessments. Cerebral small vessel disease burden was characterized using a modified Small Vessel Disease (mSVD) score. One point each was given for moderate-severe white matter hyperintensities, ≥1 cerebral microbleeds, and ≥1 lacune. Multivariable regression explored associations between mSVD score, risk factors, and cognitive performance. Results: Median age of the 228 participants (77% male) was 44.7 years (range, 23.3–63.2). In n=188 participants with consistent good quality magnetic resonance imaging sequences, mSVD scores were 0 (n=127, 68%), 1 (n=50, 27%), and 2 (n=11, 6%). Overall, one-third had an mSVD ≥1 n=61 (32%); this proportion was unchanged when adding participants with missing sequences n=72/228 (32%). The most prevalent feature was white matter hyperintensities 53/218 (24%) then cerebral microbleed 16/191 (8%) and lacunes 16/228 (7%). Older age (odds ratio, 1.10 [95% CI, 1.05–1.15], P <0.001), higher diastolic blood pressure (odds ratio, 1.05 [95% CI, 1.01–1.09], P =0.008), and a history of injection drug use (odds ratio, 3.13 [95% CI, 1.07–9.16], P =0.037) had significant independent associations with a mSVD score of ≥1 in multivariable analysis. mSVD ≥1 was associated with lower performance on tests of verbal memory, sustained attention, and decision-making, contributing 4% to 5% of the variance in each cognitive domain. Conclusions: The 32% prevalence of cerebral small vessel disease in this young, socially marginalized cohort was higher than expected for age and was associated with poorer cognitive performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".