Association Between Spinal Cord Injury and Cognitive Impairment, and the Mediating Role of Inflammation
Bibliographic record
Abstract
BACKGROUND: Spinal cord injury (SCI), typically resulting from trauma or cranial surgery, often causes motor and sensory deficits and may also impair brain function, leading to cerebral edema, elevated intracranial pressure, and cognitive decline. Growing evidence implicates systemic inflammation as a key mediator of SCI-related cognitive impairment, yet clinical validation remains limited. This study explores the association between SCI and cognitive dysfunction, with a focus on the mediating role of inflammation. METHODS: A retrospective cohort of 157 participants (SCI patients and controls) at the Affiliated Hospital of Qingdao University (January 2023-October 2024) was analyzed. Neurological impairment (ASIA) and acute-phase inflammatory markers (NLR, PLR, LMR, and SII) were assessed. MoCA evaluated cognitive function at 3 months. Logistic regression and mediation analysis quantified associations. RESULTS: SCI patients had a 151% higher risk of cognitive impairment than controls (adjusted OR=2.51, 95% CI: 1.82-3.64), especially among older adults, those with lower education, and hypertensive individuals. NLR and SII mediated 12% and 16% of the association, respectively; PLR showed a weaker effect (6%). LMR mediated 11% ( P =0.02) but had a protective direct effect (β=0.79), indicating possible compensatory mechanisms. Smoking and alcohol use further increased risk, while higher education was protective (OR=0.38). CONCLUSION: Systemic inflammation partially mediates SCI-related cognitive decline, with NLR and SII as key contributors. Blood-based inflammatory markers may aid in risk stratification and guide anti-inflammatory, lifestyle-based interventions to improve cognitive outcomes in SCI patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".