Serum neurofilament light chain is a biomarker of human spinal cord injury severity and outcome
Bibliographic record
Abstract
BACKGROUND: Neurofilaments (Nf) are major structural proteins that occur exclusively in neurons. In spinal cord injury (SCI), the severity of disease is quantified by clinical measures that have limited sensitivity and reliability, and no blood-based biomarker has been established to further stratify the degree of injury. We aimed to examine a serum-based NfL immunoassay as predictor of the clinical outcome in SCI. METHODS: Longitudinal measurement of serum NfL was performed in patients with central cord syndrome (CCS, n=4), motor-incomplete SCI (iSCI, n=10), motor-complete SCI (cSCI, n=13) and healthy controls (HC, n=67), and correlated with clinical severity, neurological outcome, and neuroprotective effect of the drug minocycline. RESULTS: Baseline NfL levels were higher in iSCI (21 pg/mL) and cSCI (70 pg/mL) than in HC (5 pg/mL, p=0.006 and p<0.001) and CCS (6 pg/mL, p=0.025 and p=0.010). Levels increased over time (p<0.001) and remained higher in cSCI versus iSCI (p=0.011) and than in CCS (p<0.001). NfL levels correlated with American Spinal Injury Association (ASIA) motor score at baseline (r=-0.53, p=0.004) and after 24 h (r=-0.69, p<0.001) and 3-12-month motor outcome (baseline NfL: r=-0.43, p=0.026 and 24 h NfL: r=-0.72, p<0.001). Minocycline treatment showed decreased NfL levels in the subgroup of cSCI patients. CONCLUSIONS: Serum NfL concentrations in SCI patients show a close correlation with acute severity and neurological outcome. Our data provide evidence that serum NfL is of prognostic value in SCI patients for the first time. Further, blood NfL levels may qualify as drug response markers in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".