Abstract TP436: Native American Veterans With Vascular Risk Factors Have High Rates of Vascular Cognitive Impairment
Bibliographic record
Abstract
Background: Rates of cardiovascular disease and stroke are elevated in Native Americans, and a greater propensity to develop vascular cognitive impairment (VCI) rather than Alzheimer-type dementia has been inferred, supporting a need for further research in VCI in this population. We determined rates and patterns of memory loss among Native American veterans with multiple vascular risk factors. Methods: Native American veterans ≥50 years old with ≥2 vascular risk factors, including smoking history, hyperlipidemia, diabetes, coronary artery disease, or peripheral arterial disease, were recruited between September 2015 and May 2016. The Montreal Cognitive Assessment (MoCA) and the Beck Depression Inventory-II were used to screen for cognitive impairment and depression. Patients with MoCA scores <26 were referred for imaging studies, memory loss serology, neuropsychiatric testing and clinical assessment by a memory loss physician. Final cognitive status was assigned by blinded adjudication. Results: We recruited 60 Native Americans aged 50-86 (mean±SD: 64±7.1 years); 90% were male, 95% had at least high-school education, and 69% had some college or advanced degrees. Risk factors included hypertension (92%), hyperlipidemia (88%), diabetes (47%), and prior/current smoking (78%). Eight (13%) with severe depression were excluded, leaving 23/51 with abnormal MoCA scores (44%, 95%CI 30%-59%). All with cognitive impairment were male compared to 83% among non-impaired subjects (p=0.059). Fifteen completed additional evaluation for memory loss, including 4/15 with normal MoCA scores who requested evaluation based on symptoms. Results were adjudicated as normal (4), or as having non-amnestic MCI (4), vascular MCI (5), and vascular dementia (2). MoCA correctly identified cognitive status in 86% (Kappa 0.66, 95%CI 0.23-1.00). Conclusions: Native American veterans have high rates of vascular cognitive impairment, which exceed rates of cognitive impairment documented in previously published older non-Native American cohorts. These results highlight the need for improved vascular risk reduction among Native American veterans. Further study is needed to identify ways to improve care in this underserved and understudied population.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".