Total cerebral small vessel disease score and cognitive performance in community‐dwelling older adults. Results from the Atahualpa Project
Bibliographic record
Abstract
Objective Cerebral small vessel disease (SVD) is inversely associated with cognitive performance. However, whether the total SVD score is a better predictor of poor cognitive performance than individual signatures of SVD is inconclusive. We aimed to estimate the combined and independent predictive power of these MRI findings. Methods Atahualpa residents aged ≥60 years underwent brain MRI. Cognitive performance was measured by the Montreal Cognitive Assessment (MoCA). The presence of moderate‐to‐severe white matter hyperintensities, deep cerebral microbleeds, lacunar infarcts, and >10 enlarged perivascular spaces was added for estimating the total SVD score ranging from 0 to 4 points. Montreal Cognitive Assessment predictive models were fitted to assess how well the total SVD score or each of its components predicts cognitive performance. Results Of 351 eligible candidates, 331 (94%) were included. The total SVD score was 0 points in 202 individuals (61%), 1 point in 67 (20%), 2 points in 40 (12%), 3 points in 15 (5%), and 4 points in seven (2%). A generalized lineal model showed an inverse relationship between the total SVD score and the MoCA (p = 0.015). The proportion of variance in the MoCA score explained by the SVD score was 32.8% (R2 = 0.328). This predictive power was similar for white matter hyperintensities (R2 = 0.306), microbleeds (R2 = 0.313), lacunar infarcts (R2 = 0.323), and perivascular spaces (R2 = 0.313). Conclusions This study shows a significant association between the SVD score and worse cognitive performance. The SVD score is a predictor of poor cognitive performance. This predictive power is not better than that of isolated neuroimaging signatures of SVD. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".