Associations between systemic inflammation and cognitive trajectories post-stroke
Bibliographic record
Abstract
Our objective was to explore whether plasma inflammatory biomarkers and related metabolites in acute phase and 3 months after stroke were associated with different cognitive trajectories and with changes in cognition post-stroke. The Norwegian Cognitive Impairment After Stroke (Nor-COAST) study was a prospective, multicentre cohort study of patients with acute stroke, followed up at 3, 18, and 36 months post-stroke. First, we modelled cognitive trajectory groups based on Montreal Cognitive Assessment (MoCA) scores and used multinominal logistic regression to study the associations between systemic inflammatory biomarkers/metabolites and group membership. Second, using mixed linear regression, we investigated whether the same biomarkers/metabolites were associated with changes in MoCA scores over time, stratified by pre-stroke cognitive status. The 466 participants had mean (SD) age 72 (12) years, 59% were males, and mean (SD) NIHSS score at admittance was 4 (4.8). Higher acute-phase values of the terminal complement complex, interleukin 6, macrophage inflammatory protein 1α, neopterin, quinolinic acid, and PA ratio = 4-pyridoxic acid / (pyridoxal + pyridoxal 5'-phosphate) and higher 3-month values of neopterin were associated with increased risk of being in the group characterized by low and declining MoCA score compared to the group of best MoCA score (p < 0.01). Higher acute-phase values of tumour necrosis factor and interleukin 8, were associated with progressive decline in the MoCA score (p < 0.01). Premorbid factors, and in particular pre-stroke frailty, had more impact on the models than stroke-related factors, and partly confounded several of these associations. Higher degrees of systemic inflammation in the acute phase were associated with worse cognitive trajectories and may reflect the response to the acute stroke, stroke-related complications and/or premorbid conditions.Trial registration: ClinicalTrials.gov: NCT02650531. Retrospectively registered January 8, 2016. First participant included May 18, 2015.
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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 | 0.000 | 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) | 0.001 | 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".