Cerebrospinal Fluid Inflammatory Cytokines and Biomarkers of Injury Severity in Acute Human Spinal Cord Injury
Bibliographic record
Abstract
There is an urgent need for both the scientific development and clinical validation of novel therapies for acute spinal cord injury (SCI). The scientific development of novel therapies would be facilitated by a better understanding of the acute pathophysiology of human SCI. Clinical validation of such therapies would be facilitated by the availability of biomarkers with which to stratify injury severity and predict neurological recovery. Cerebrospinal fluid (CSF) samples were obtained over a period of 72 h in 27 patients with complete SCI (ASIA A) or incomplete SCI (ASIA B or C). Cytokines were measured in CSF and serum samples using a multiplex cytokine array system and standard enzyme-linked immunosorbent assay (ELISA) techniques. Neurological recovery was monitored, and patient-reported neuropathic pain was documented. IL-6, IL-8, MCP-1, tau, S100beta, and glial fibrillary acidic protein (GFAP) were elevated in a severity-dependent fashion. A biochemical model was established using S100beta, GFAP, and IL-8 to predict injury severity (ASIA A, B, or C). Using these protein concentrations at 24-h post injury, the model accurately predicted the observed ASIA grade in 89% of patients. Furthermore, segmental motor recovery at 6 months post injury was better predicted by these CSF proteins than with the patients' baseline ASIA grade. The pattern of expression over the first 3 to 4 days post injury of a number of inflammatory cytokines such as IL-6, IL-8, and MCP-1 provides invaluable information about the pathophysiology of human SCI. A prediction model that could use such biological data to stratify injury severity and predict neurological outcome may be extremely useful for facilitating the clinical validation of novel treatments in acute human 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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 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".