Predicting Injury Severity and Neurological Recovery after Acute Cervical Spinal Cord Injury: A Comparison of Cerebrospinal Fluid and Magnetic Resonance Imaging Biomarkers
Bibliographic record
Abstract
Biomarkers of acute human spinal cord injury (SCI) could provide a more objective measure of spinal cord damage and a better predictor of neurological outcome than current standardized neurological assessments. In SCI, there is growing interest in establishing biomarkers from cerebrospinal fluid (CSF) and from magnetic resonance imaging (MRI). Here, we compared the ability of CSF and MRI biomarkers to classify injury severity and predict neurological recovery in a cohort of acute cervical SCI patients. CSF samples and MRI scans from 36 acute cervical SCI patients were examined. From the CSF samples taken 24 h post-injury, the concentrations of inflammatory cytokines (interleukin [IL]-6, IL-8, monocyte chemotactic protein-1), and structural proteins (tau, glial fibrillary acidic protein, and S100β) were measured. From the pre-operative MRI scans, we measured intramedullary lesion length, hematoma length, hematoma extent, CSF effacement, cord expansion, and maximal spinal cord compression. Baseline and 6-month post-injury assessments of American Spine Injury Association Impairment Scale (AIS) grade and motor score were conducted. Both MRI measures and CSF biomarker levels were found to correlate with baseline injury grade, and in combination they provided a stronger model for classifying baseline AIS grade than CSF or MRI biomarkers alone. For predicting neurological recovery, the inflammatory CSF biomarkers best predicted AIS grade conversion, whereas structural biomarker levels best predicted motor score improvement. A logistic regression model utilizing CSF biomarkers alone had a 91.2% accuracy at predicting AIS conversion, and was not strengthened by adding MRI features or even knowledge of the baseline AIS grade. In a direct comparison of MRI and CSF biomarkers, the CSF biomarkers discriminate better between different injury severities, and are stronger predictors of neurological recovery in terms of AIS grade and motor score improvement. These findings demonstrate the utility of measuring the acute biological responses to SCI as biomarkers of injury severity and neurological prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".