Early cognitive dysfunction development immunological predictors in the ischemic stroke acute period
Bibliographic record
Abstract
The purpose of the study is activity identification CXC family cytokines in patients with cognitive impairments in the acute period of ischemic stroke. Material and methods. 78 patients with diagnosis “Ischemic stroke” were examined. Depending on cognitive impairment (Montreal Cognitive Assessment (MoCA)) patients were divided into two groups: the 1st group — 58 patients with cognitive decline (MoCA ≤ 25 points); the 2nd group — 20 patients without cognitive decline. Neuropsychologic testing was performed on the second day of hospitalisation and included episodic memory, executive function, speech, gnosis, praxis and IQCODE parameters examination. Laboratory diagnosis consisted of level assessing of CXC family chemokines (CXCL10, CXCL11, CXCL9, CXCL1, CXCL8) and TNF-α cytokine in patients’ plasma on the second day of hospitalisation. Statistical analysis was employed using the Python programing language and its libraries Pandas and SciPy. Results. Statistical analysis revealed the highest level of IP-10/CXCL10 chemokines (p = 0.002) and Gro-a/CXCL1 (p = 0.044) in patients of the 1st group, statistically significant correlations of MoCA and IQCODE with IP-10/CXCL10 and Gro-a/CXCL1 concentrations, correlations of IP-10/CXCL10 concentrations with semantic information processing functions (r = –0.512), subject gnosis (r = –0.211), memory (r = 0.275), speech (r = –0.400), and Gro-a/ CXCL1 level with semantic information processing (r = –0.418). Conclusion. The study of chemokines of the CXC cluster represents a relevant and promising direction in the diagnosis and assessment of progression of early post-stroke cognitive impairment of mixed genesis due to minimal invasiveness and high specificity. Further studies are needed to verify CXCL chemokines, particularly IP-10/CXCL10 and Gro-a/CXCL1 as potential molecular markers of neurological damage in neurodegenerative and inflammatory diseases of the central nervous system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".