Abstract TP38: Cognitive Decline After Stroke Correlates With Small Vessel Disease and Race
Bibliographic record
Abstract
Introduction: An Asian study found 30% of patients showed a cognitive decline measured by Montreal Cognitive Assessment (MoCA) score one year after stroke. There is limited US data on this subject. We investigated 1) decline rate in MoCA one year post-stroke 2) racial differences in baseline MoCA post-stroke and 3) if racial differences in small vessel disease (SVD) correlated with the decline in MoCA. Methods: MoCA was assessed in the PREMIERS trial (NCT#02541032) at baseline and one year after index stroke. Sub-scores for each MoCA domain were combined into a total score. The Fazekas scale was used to rate white matter signal abnormalities on patient MRI FLAIR images at baseline. Patients were categorized by total, deep white matter (DWM), and periventricular white matter scores. They were then sub-categorized into none/mild or moderate/severe groups. A one year cognitive decline was defined as a decline in MoCA scores by ≥2 points. Results: Of 280 patients, 30.4% had a ≥2 decline in MoCA score, with no significant difference between AA and white (29.4% vs. 32.9%, p=0.57). However, AA patients had lower baseline total MoCA scores (20.4±4.6 vs. 22.6±3.9, p<0.001), visuospatial/executive (3.1±1.5 vs. 3.7±1.3, p<0.001), naming (2.5±0.7 vs. 2.8±0.4, p=0.001), and attention (4.2±2.1 vs. 4.9±1.9, p<0.001) sub-scores. Overall, baseline MoCA was lower in patients with moderate/severe total Fazekas scores (20.1±5.0 vs. 21.6±4.2, p=0.015) and DWM sub-scores (20.1±5.2 vs. 21.4±4.2, p=0.048). In AA, baseline MoCA remained lower in patients with moderate/severe total Fazekas scores (19.6±5.1 vs. 21.0±4.2, p=0.039) and DWM sub-scores (19.4±5.5 vs. 21.0±4.1, p=0.055). This was contrary to White patients with moderate/severe total Fazekas scores (22.3. ±4.4 vs. 23.0±3.9, p=0.50) and DWM sub-scores (23.1±2.3 vs. 22.7±4.4, p=0.73). Conclusion: We report that 30.4% of patients in this US-based study showed a decline in MoCA score one year post-stroke, which aligns with a similar study conducted in Asia. A significant association was found between the AA race and lower baseline total MoCA score compared to white patients. Moderate/severe total and DWM Fazekas scores are also significantly associated with lower baseline MoCA scores, a finding specific to AA patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".