Cognitive Decline After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Inflammation may contribute to cognitive impairment after stroke. Inflammatory markers are associated with hippocampal atrophy. We tested whether markers of inflammation, erythrocyte sedimentation rate (ESR), and serum levels of C-reactive protein are associated with reduced hippocampal volume and poor cognitive performance among stroke survivors. METHODS: We analyzed 368 consecutive cases from our prospective study of first-ever mild-moderate stroke patients. MRI, cognitive tests, and inflammatory markers were determined. Patients were reevaluated 6 and 12 months after the event. RESULTS: ESR remained unchanged in follow-up examinations, suggesting a chronic inflammation background in some patients. Higher levels of C-reactive protein and ESR were associated with worse performance in cognitive tests, particularly memory scores. This association was maintained for ESR (but not C-reactive protein) after adjustment for confounders (P=0.002). Patients with smaller hippocampi had inferior cognitive results. Moreover, in a multivariate regression model, higher ESR values (but not C-reactive protein) were related to reduced hippocampal volume (P=0.049). CONCLUSIONS: This report shows a strong relationship between ESR and hippocampal volume, as well as with cognitive performance among poststroke patients. This could plausibly relate to incipient cognitive decline via hippocampal pathways.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".