Abstract WMP49: White Matter Hyperintensities in a High Risk Population Living in Marginal Housing (HOTEL study)
Bibliographic record
Abstract
Background: White Matter Hyperintensities (WMH) are features of cerebral small vessel disease (cSVD) along with lacunes, cerebral microbleeds and perivascular spaces. Vascular risk factors account for only a small proportion of the variability of the presence of WMH, and the role of additional risk factors including drug use/dependence or infections is not well defined. Objective: Examine prevalence and risk factors associated with WMH of presumed vascular origin within the HOTEL cohort, a population living in marginal housing with a high prevalence of prior homelessness, substance dependence, head trauma, mental illness and infectious diseases. Methods: Baseline imaging on 3T MRI included T1, T2-FLAIR and SWI sequences. WMH not consistent with vascular origins were excluded. Two raters assessed WMH using the Fazekas scale. Participants were divided into those with or without moderate-severe WMH (periventricular Fazekas score >2 or deep score >1). Potential cSVD risk factors which were significant on univariate analysis were entered into a multivariable stepwise binomial logistic regression to identify independent risk factors for moderate-severe WMH. Results: Intraclass coefficient for inter-rater reliability was 0.948 (95% CI, 0.924 to 0.965) for periventricular WMH and 0.848 (95% CI, 0.782 to 0.895) for deep WMH. Baseline prevalence of moderate-severe WMH (mean age 43.6 ± 9.5 years, 78% male) was 24.5%, much higher than in other, older healthy aging cohorts (Table). Age (OR 1.085, 95%CI 1.042-1.130), systolic blood pressure (OR 1.033, 95%CI 1.008-1.058) and regular injection drug use (OR 3.655, 95%CI 1.284-10.403) together explained 23.5% of variance in the presence of moderate-severe WMH within this population, with injection drug use having the largest effect. Conclusions: This young cohort appears to have an accelerated burden of cSVD, with injected drug use as a major risk factor. Further research is needed to elucidate potential mechanisms.
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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 | 0.001 | 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".