Relationship Between Lymphocyte-Associated Inflammatory Markers and Post-Stroke Cognitive Impairment
Bibliographic record
Abstract
Qian-Ying Hu,1,* Juan Liu,1,* Cai-Hong Cui,1 Mei-Fang Guo,2 Yu-Tong Shi,2 Xiao-Man Zhang,2 Bing-Fei Jia,2 Xin-Yu Li,2 Su-Juan Sun3 1Department of Rehabilitation Medicine, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, People’s Republic of China; 2Department of Basic Medical Sciences, Hebei University, Baoding, Hebei, 071000, People’s Republic of China; 3Department of Nursing, Hebei General Hospital, Shijiazhuang, Hebei, 050000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Su-Juan Sun, Department of Nursing, Hebei General Hospital, No. 348 Heping West Road, Xinhua District, Shijiazhuang, Hebei, 050000, People’s Republic of China, Tel +86 13933093071, Email sujuansunssjm@126.com Cai-Hong Cui, Department of Rehabilitation Medicine, Affiliated Hospital of Hebei University, No. 212 of Yuhua East Road, Lianchi District, Baoding, Hebei, 071000, People’s Republic of China, Tel +86 13463236473, Email caihongcuicchk@126.comObjective: To determine whether differences in lymphocyte-related inflammatory markers in the ultra-early phase of stroke (within 24 hours of onset) are associated with post-stroke cognitive impairment in the early recovery phase (within 30 days of stroke onset), and to further assess the predictive value of these markers.Methods: The study population consisted of patients who underwent rehabilitation treatment at the Rehabilitation Department of Hebei University Affiliated Hospital between December 2024 and June 2025, within 30 days of stroke onset, ie, during the early recovery phase of stroke. Patients were grouped based on whether they developed cognitive impairment. A retrospective analysis was conducted of patients’ blood markers and neurological deficit scores within 24 hours of stroke onset to examine the relationship between ultra-early blood markers and neurological deficits and post-stroke cognitive impairment.Results: There were no significant differences in baseline data between the two groups. However, the proportion of hemorrhagic stroke patients was significantly higher in the PSCI group than in the non-PSCI group (39.7% vs 18.8%, P=0.026< 0.05). NLR and NIHSS scores showed significant differences between the two groups. Multivariate analysis indicated that NIHSS (OR=1.297, 95% CI: 1.167– 1.442, p< 0.001) was independently associated with PSCI, while NLR (OR=1.107, 95% CI: 0.995– 1.231, p=0.063) showed a borderline association with PSCI. MLR showed differences between the two groups in univariate analysis (P=0.018) but was excluded in multivariate analysis. ULR did not show significant differences.Conclusion: NIHSS is a strong predictive factor (P < 0.05), with a cut of value of 12 calculated by the ROC curve. NLR is at the threshold for an independent risk factor. Subsequent ROC curves indicate that NLR has low diagnostic sensitivity but high specificity, making it more suitable for screening rather than diagnostic use. MLR and ULR did not demonstrate high predictive value; further studies should be conducted to expand the sample size, perform subgroup analyses, and increase follow-up.Keywords: post-stroke cognitive impairment, NIHSS, NLR, MLR, ULR
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".