Serum biomarkers in the diagnosis and prognosis of traumatic spinal cord injury: A systematic review and meta-analysis
Bibliographic record
Abstract
Traumatic spinal cord injury (TSCI) is a severe neurological condition that frequently leads to permanent disability. Serum inflammatory markers and structural proteins may serve as potential biomarkers for TSCI. The present study aimed to evaluate the diagnostic and prognostic value of serum biomarkers in TSCI. In this article, a comprehensive literature search was conducted using databases such as Wanfang, VIP Database, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, PubMed, Cochrane Library, Embase, and Web of Science. Meta-analysis was performed using RevMan 5.4 software to compare serum biomarker concentrations between TSCI patients and healthy controls (diagnostic group) and between patients with favorable and unfavorable prognoses (prognostic group). The quality of the included studies was evaluated using the Newcastle–Ottawa Scale. The results showed that: (1) In the diagnostic group, the meta-analysis revealed that serum levels of NSE, MIF, S100β, TNF-α, IL-1β, IL-4, IL-6, IL-16, CCL2, CCL4, CCL21, CXCL1, CXCL9, CXCL10, and CXCL12 were significantly elevated in TSCI patients compared with healthy controls, while IL-10 levels were decreased. (2) In the prognostic group, meta-analysis indicated that serum GFAP and NSE concentrations were significantly lower in patients with favorable prognoses than in those with poor prognoses. In conclusion, the serum levels of most structural proteins and inflammatory factors in patients with TSCI are elevated compared with healthy controls, and patients with poor prognosis exhibit even higher concentrations than those with favorable outcomes. These findings indicate the potential value of these markers for diagnosing TSCI and assessing prognosis.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".