Abstract 47: Renal Function, MRI Brain Changes and Post-stroke Cognitive Impairment
Bibliographic record
Abstract
Background and Purpose: Limited data exist regarding the relationship between chronic kidney disease (CKD) and post-stroke cognitive impairment. We aimed to evaluate the impact of impaired renal function on markers of cerebral small vessel disease, brain pathology and cognitive decline in a longitudinal post-stroke cohort. Methods: The TABASCO study is a prospective cohort of mild-moderate ischemic stroke/TIA patients, who underwent a 3T MRI, and were assessed for their cognitive function at hospital admission, 6, 12 and 24 months following stroke, using the Montreal Cognitive Assessment (MoCA) and a computerized cognitive testing battery. Estimated renal function was evaluated at admission using the Cockcroft Gault creatinine clearance (CCl) equation. The volume and integrity of preexisting white matter hyperintensity (WMH), ischemic lesions and brain atrophy on MRI were measured. Results: Baseline data were available for 462 subjects (mean age 67.4 years, 60.4% males). Participants with a CCl <60 ml/min performed significantly worse in all cognitive tests over time (p<0.001) than those with a CCl ≥60 ml/min. CKD was also associated with enlarged WMH volume (p<0.001), cortical atrophy (p=0.002) and smaller hippocampal volume (p<0.001). After the 2-years follow-up, 16% of the participants developed cognitive impairment. Multiple logistic regression analysis controlling for traditional risk factors, including cardiovascular, showed a significant association of CCl <60 ml/min at baseline with development of cognitive impairment at the end of follow-up [odds ratio: 2.99 (95% confidence interval: 1.08-8.27), p = 0.035]. Conclusions: Decreased renal function is associated with increased WMH volume and cortical atrophy, known biomarkers of the aging brain, and is a predictor of lower performances in cognitive tests and cognitive decline 2 years after stroke/TIA. CKD may contribute to cerebral small vessel disease that underlies post-stroke cognitive decline, suggesting a new target for early intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".