Plasma Amyloid Beta and Tau Levels Are Predictors of Post-stroke Cognitive Impairment: A Longitudinal Study
Bibliographic record
Abstract
Objectives: Post-stroke cognitive impairment (PSCI) is a common disease that may occur within 3 months after a stroke or even later. However, the mechanism of PSCI development is unclear. The present study investigated whether the levels of plasma amyloid beta-42 (Aβ42) and tau are associated with the onset of PSCI. Methods: Fifty-five patients admitted within 7 days of acute ischemic stroke were enrolled and followed up for 1 year. Montreal Cognitive Assessment (MoCA) was administered at 3 months and 1 year, and plasma Aβ42 and tau levels were determined using an ultrasensitive immunoassay (immunomagnetic reduction) within 7 days of the stroke event and 3 months later. Results: In this study, 13 of 55 patients developed PSCI (MoCA score <23) at 3 months. Seven patients with PSCI at 3 months recovered to a cognitively normal state at 1 year, whereas seven cognitively normal patients developed PSCI at 1 year. The patients with PSCI at 1 year had a higher incidence of cognitive function deterioration between 3 months and 1 year compared with those without PSCI at 1 year. Plasma Aβ42 and tau levels at 3 months were lower in the patients with PSCI at 1 year than in those without PSCI (Aβ42: 15.1 vs. 17.2 pg/mL, P = 0.013; tau: 16.7 vs. 19.9 pg/mL, P = 0.018). Low education levels and pre-existing white matter disease were the most significant predictors of PSCI at 3 months, and poor cognitive performance at 3 months and low plasma Aβ42 and tau levels at 3 months were the most significant predictors of PSCI at 1 year. Conclusion: The pathogenesis of PSCI is complex and changes with time. Ischemia-induced Aβ42/tau pathology might be involved in PSCI development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".