Inflammatory and structural biomarkers in acute traumatic spinal cord injury
Bibliographic record
Abstract
The paralysis of an acute spinal cord injury (SCI) remains a catastrophic condition for which there are currently no effective treatments. While the diagnosis of acute traumatic SCI is typically quite easy to make, distinguishing the exact degree of severity and prognosticating the extent of neurologic recovery are challenging. Functional neurologic measures are currently used to stratify injury severity and predict neurologic outcome. However, these measures are often impossible to determine in acutely injured patients. Additionally, for patients deemed to be of a specific injury severity, the variability in spontaneous neurologic recovery is high. Both of these issues severely impair the ability to perform clinical trials in novel therapies for SCI. Biomarkers that could more precisely define the severity of injury and better predict neurologic outcome would be extremely valuable. Furthermore, biological surrogate outcomes measures would be very useful in small preliminary clinical trials of novel therapies if they could inform decisions around the therapeutic regimen for subsequent larger clinical trials. This review highlights our ongoing work in establishing biomarkers for SCI using cerebrospinal fluid samples from acutely injured patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".