Trajectories of post-stroke cognitive function related to systemic inflammatory biomarkers and metabolites: The Nor-COAST study
Bibliographic record
Abstract
We have previously shown that cytokines, the complement system, neopterin, the kynurenine pathway, and vitamin B6-indexes were associated with post-stroke cognitive impairment. In this study, we aimed to investigate whether these biomarkers and metabolites are associated with different trajectories of cognitive function post-stroke using Montreal Cognitive Assessment (MoCA) scale. The Norwegian Cognitive Impairment After Stroke study (Nor-COAST) is a prospective multicentre cohort study of patients with acute stroke, recruited from 2015 through 2017. The present study included participants with ischemic stroke and measurements of inflammatory biomarkers and metabolites in plasma. Included participants had performed MoCA at baseline and at least one additional follow-up. Trajectories of post-stroke cognitive function was modelled utilizing MoCA scores from baseline, 3 months, 18 months, and 36 months post-stroke. We used multinominal logistic regression with trajectory groups as dependent variable and inflammatory biomarkers and metabolites (cytokines, a complement marker, neopterin, metabolites from the kynurenine pathway, and vitamin-B6-indexes) at baseline as covariates in a model adjusted for age, sex, creatinine and hospital. 401 participants were included. Mean age (SD) 71.3 (11.6) years, 59 % males, and mean (SD) NIHSS score at day 1 after hospital admission 2.5 (3.3). We identified three trajectory groups of post-stroke cognitive function: “Low and declining” (11 %), “Moderate and stable” (33%), and “High and increasing” (56 %). High baseline levels of neopterin, quinolinic acid, the PAr-index, the terminal complement complex, interleukin 6 and macrophage inflammatory protein 1α were associated with increased risk of being in group “Low and declining” compared to “High and increasing” (p<0.05). Preliminary results show that higher levels of systemic inflammatory biomarkers and metabolites were associated with post-stroke cognitive dysfunction.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".