Elevated Serum Amyloid A Is Associated With Cognitive Impairment in Ischemic Stroke Patients
Bibliographic record
Abstract
Background and Purpose The impact of serum amyloid A on cognitive impairment after ischemic stroke is unclear. We aimed to investigate the association between serum amyloid A (SAA) levels and post-stroke cognitive impairment (PSCI) at 3 months after ischemic stroke. Methods One hundred and ninety-eight patients were enrolled prospectively from June 2020 to April 2021. The SAA concentrations were measured using a commercially available enzyme-linked immunosorbent assay kit after admission. Cognitive function was assessed using the Montreal Cognitive Assessment score at 3 months after the symptom onset. We defined a Montreal Cognitive Assessment score <25 as cognitive impairment. Results During 3-month follow-up, 80 patients (40.4%) were diagnosed as having PSCI. As compared with patients with cognitively normal ischemic stroke, those with PSCI were older, more likely to have diabetes and white matter lesions, and had a higher baseline National Institutes of Health stroke score and SAA levels. After adjustment for age, the National Institutes of Health stroke score and other covariates, the OR for the highest quartile of SAA compared with the lowest quartile was 5.72 (95% CI, 2.17–15.04, P = 0.001) for PSCI. Also, ordinal logistic regression analysis showed that higher SAA concentrations were associated with increased risk of PSCI severity (OR, 4.31; 95% CI, 1.81–10.33, P = 0.001). Similar results were found when the SAA levels were analyzed as a continuous variable. Conclusions This present study demonstrated that increased SAA levels might be associated with PSCI at 3 months after ischemic stroke.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".