Measurement of carotid plaque burden: A tool for predicting and preventing dementia?
Bibliographic record
Abstract
Carotid plaque burden is a strong predictor of stroke risk, and preventing stroke reduces the risk of dementia. Treating carotid plaque burden markedly reduces the risk of stroke. Among patients age 65–80 years attending a stroke prevention clinic, we identified those with a carotid plaque burden in the top 20% of Total Plaque Area (High TPA) and the bottom 20% (Low TPA) and performed cognitive tests: The Montreal Cognitive Assessment test (MoCA), the WAIS-III Digit Symbol-Coding Test (DSST) and Trail-Making Test (TMT) part A and B. There were 31 patients recruited; 11 Low TPA (5 men) and 20 High TPA (17 men), p = 0.04. TPA was 35 ± 25 mm2 in the Low TPA vs.392 ± 169 mm2 in the High TPA group (0.0001). Patients with a high plaque burden had significantly worse performance on all the cognitive tests, all p< 0.05 A high carotid plaque burden identifies patients at risk of cognitive impairment. Because carotid plaque burden is treatable, and treating it markedly reduces the risk of stroke, we suggest that measurement of plaque burden is a useful tool for both prediction of cognitive impairment, and prevention of dementia.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".