Serum CCL21 as a Potential Biomarker for Cognitive Impairment in Spinal Cord Injury
Bibliographic record
Abstract
Objective. Cognitive impairment is considered to be an important complication of spinal cord injury (SCI), but its underlying mechanism remains unclear. The purpose of this study is to explore whether serum CCL21 can be used as a potential biomarker of cognitive impairment in SCI. Methods. In Neck‐Shoulder and Lumbocrural Pain Hospital, Shandong First Medical University & Shandong Academy of Medical Sciences, hospitalized or treated acute SCI patients were included in the study as the SCI group (SCI). At the same time, a normal control group (NC) matching the age and sex of the SCI group was recruited in the outpatient clinic. Once the two groups were enrolled, their demographics and clinical characteristics were collected immediately. Enzyme‐linked immunosorbent assay (ELISA) was used to detect serum CCL21 levels within 24 hours of admission. Three months later, the Montreal Cognitive Assessment (MoCA) was used to test the cognitive function of the population. Results. A total of 84 SCI patients and 49 NC populations were eligible for inclusion in the study. There was no significant statistical difference in the demographics and clinical characteristics (age, gender, BMI, TG, LDL‐C, FBG, SBP, and DBP) between the two groups (p > 0.05). Compared with the NC group, the SCI group had a higher serum CCL21 level (p < 0.001) and a lower MoCA score (p < 0.001). Serum CCL21 level in SCI was negatively correlated with MoCA score (p = 0.023). Multivariable analyses showed that serum CCL21 level is an independent prognostic factor of cognitive impairment in SCI. Conclusions. MoCA score has a linear relationship with serum CCL21 quartile, and SCI cognitive function has a negative correlation with serum CCL21. Serum CCL21 is an independent risk factor for cognitive impairment after SCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".