Novel Indices of Cognitive Impairment and Incident Cardiovascular Outcomes in the REWIND Trial
Bibliographic record
Abstract
CONTEXT: Low cognitive scores are risk factors for cardiovascular outcomes. Whether this relationship is stronger using novel cognitive indices is unknown. METHODS: Participants in the Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) trial who completed both the Montreal Cognitive Assessment (MoCA) score and Digit Substitution Test (DSST) at baseline (N = 8772) were included. These scores were used to identify participants with baseline substantive cognitive impairment (SCI), defined as a baseline score on either the MoCA or DSST ≥ 1.5 SD below either score's country-specific mean, or SCI-GM, which was based on a composite index of both scores calculated as their geometric mean (GM), and defined as a score that was ≥ 1.5 SD below their country's average GM. Relationships between these measures and incident major adverse cardiovascular events (MACE), and either stroke or death were analyzed. RESULTS: Compared with 7867 (89.7%) unaffected participants, the 905 (10.3%) participants with baseline SCI had a higher incidence of MACE (unadjusted hazard ratio [HR] 1.34; 95% CI 1.11, 1.62; P = 0.003), and stroke or death (unadjusted HR 1.60; 95% CI 1.33, 1.91; P < 0.001). Stronger relationships were noted for SCI-GM and MACE (unadjusted HR 1.61; 95% CI 1.28, 2.01; P < 0.001), and stroke or death (unadjusted HR 1.85; 95% CI 1.50, 2.30; P < 0.001). For SCI-GM but not SCI, all these relationships remained significant in models that adjusted for up to 10 SCI risk factors. CONCLUSION: Country-standardized SCI-GM was a strong independent predictor of cardiovascular events in people with type 2 diabetes in the REWIND trial.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".