Cerebrospinal Fluid Biomarkers To Stratify Injury Severity and Predict Outcome in Human Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Neurologic impairment after spinal cord injury (SCI) is currently measured and classified by functional examination. Biological markers that objectively classify injury severity and predict outcome would greatly facilitate efforts to evaluate acute SCI therapies. The purpose of this study was to determine how well inflammatory and structural proteins within the cerebrospinal fluid (CSF) of acute traumatic SCI patients predicted American Spinal Injury Association Impairment Scale (AIS) grade conversion and motor score improvement over 6 months. Fifty acute SCI patients (29 AIS A, 9 AIS B, 12 AIS C; 32 cervical, 18 thoracic) were enrolled and CSF obtained through lumbar intrathecal catheters to analyze interleukin (IL)-6, IL-8, monocyte chemotactic protein (MCP)-1, tau, S100β, and glial fibrillary acidic protein (GFAP) at 24 h post-injury. The levels of IL-6, tau, S100β, and GFAP were significantly different between patients with baseline AIS grades of A, B, or C. The levels of all proteins (IL-6, IL-8, MCP-1, tau, S100β, and GFAP) were significantly different between those who improved an AIS grade over 6 months and those who did not improve. Linear discriminant analysis modeling was 83% accurate in predicting AIS conversion. For AIS A patients, the concentrations of proteins such as IL-6 and S100β correlated with conversion to AIS B or C. Motor score improvement also was strongly correlated with the 24-h post-injury CSF levels of all six biomarkers. The analysis of CSF can provide valuable biological information about injury severity and recovery potential after acute SCI. Such biological markers may be valuable tools for stratifying individuals in acute clinical trials where variability in spontaneous recovery requires large recruitment cohorts for sufficient power.
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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 | 0.000 | 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 | 0.001 | 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".