The Relationship Between Glucose Control and Cognitive Function in People With Diabetes After a Lacunar Stroke
Bibliographic record
Abstract
CONTEXT: Lacunar strokes and diabetes are risk factors for cognitive dysfunction. Elucidating modifiable risk factors for cognitive dysfunction has great public health implications. One factor may be glycemic status, as measured by glycated hemoglobin (A1c). OBJECTIVE: The aim of this study was to assess the relationship between A1c and cognitive function in lacunar stroke patients with diabetes. METHODS: The effect of baseline and follow-up A1c on the baseline and the change in Cognitive Assessment Screening Instrument (CASI) score over time among participants with a median of 2 cognitive assessments (range, 1-5) was examined in 942 individuals with diabetes and a lacunar stroke who participated in the Secondary Prevention of Small Subcortical Strokes (SPS3) trial (ClinicalTrials.gov No. NCT00059306). RESULTS: Every 1% higher baseline A1c was associated with a 0.06 lower standardized CASI z score (95% CI, -0.101 to -0.018). Higher baseline A1c values were associated with lower CASI z scores over time (P for interaction = .037). A 1% increase in A1c over time corresponded with a CASI score decrease of 0.021 (95% CI, -0.0043 to -0.038) during follow-up. All these remained statistically significant after adjustment for age, sex, education, race, depression, hypertension, hyperlipidemia, body mass index, cardiovascular disease, obstructive sleep apnea, diabetic retinopathy, nephropathy insulin use, and white-matter abnormalities. CONCLUSION: This analysis of lacunar stroke patients with diabetes demonstrates a relationship between A1c and change in cognitive scores over time. Intervention studies are needed to delineate whether better glucose control could slow the rate of cognitive decline in this high-risk 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".