MétaCan
Menu
Back to cohort
Record W4409767961 · doi:10.3389/fneur.2025.1565613

Predictive value of circulating inflammatory biomarkers for early-onset post-stroke cognitive impairment: a prospective cohort study

2025· article· en· W4409767961 on OpenAlexaboutno aff
Weiquan Huang, Libin Liao, Qian Liu, Rongchao Ma, Wenwei Hu, Yuan Dai, Luna Wang, Dujuan Sha

Bibliographic record

VenueFrontiers in Neurology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsnot available
FundersNanjing Drum Tower Hospital
KeywordsMedicineProspective cohort studyPredictive valueCognitive impairmentStroke (engine)Internal medicineCohortCognitionBiomarkerPsychiatryDisease

Abstract

fetched live from OpenAlex

Introduction Stroke ranks as the second leading cause of mortality and the third leading cause of disability globally. Post-stroke cognitive impairment (PSCI) is a prevalent complication following acute ischemic stroke, imposing substantial burdens on patients, families, and society. This study aimed to explore the potential of circulating immune-inflammatory markers as predictors of PSCI. Methods Conducted as a prospective observational cohort study from June 2023 to August 2024 at the Affiliated Drum Tower Hospital, Medical School of Nanjing University, it included patients experiencing their first acute ischemic stroke within 72 h of symptom onset. Cognitive assessments were conducted 7 to 10 days post-stroke using the Montreal Cognitive Assessment (MoCA), with scores below 23 indicating PSCI. Results A total of 146 patients meeting the inclusion criteria were recruited, with 71 patients exhibiting PSCI during the acute phase of stroke. Compared to patients in the post-stroke no cognitive impairment (PSNCI) group, those with PSCI demonstrated significantly elevated peripheral blood neutrophil-to-lymphocyte ratio (NLR), globulin-to-lymphocyte ratio (GLR), and C-reactive protein-to-lymphocyte ratio (CLR), while the lymphocyte-to-monocyte ratio (LMR) was notably reduced (all p < 0.05). Both univariate and multivariate logistic regression analyses identified GLR as independently associated with PSCI. After adjusting for common clinical variables, the odds ratio (OR) for the highest tertile of GLR compared to the lowest was 6.20 (95% CI, 2.10–18.33; p = 0.001). The optimal GLR cutoff was 18.22, with a sensitivity of 62.0%, specificity of 78.7%, and an area under curve (AUC) of 0.726. Conclusion This study indicates that elevated circulating GLR levels during the acute phase post-stroke onset are an independent risk factor for early-onset PSCI, even after adjusting for clinically relevant variables.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.249
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in NeurologySame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207