Serum Albumin Predicts Long-Term Neurological Outcomes After Acute Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: There is a need to identify reliable biomarkers of spinal cord injury recovery for clinical practice and clinical trials. OBJECTIVE: Our objective was to correlate serum albumin levels with spinal cord injury neurological outcomes. METHODS: We performed a secondary analysis of patients with traumatic spinal cord injury (n = 591) participating in the Sygen clinical trial. Serum albumin concentrations were obtained as part of routine blood chemistry analysis, at trial entry (24-72 hours), 1, 2, and 4 weeks after injury. The primary outcomes were "marked recovery" and lower extremity motor scores, derived from the International Standards for the Neurological Classification of Spinal Cord Injury. Data were analyzed with multivariable logistic and linear regression to adjust for potential confounders. RESULTS: Serum albumin was significantly associated with spinal cord injury neurological outcomes. Higher serum albumin concentrations at 1, 2, and 4 weeks were associated with higher 52-week lower extremity motor score. Similarly, the odds of achieving "marked neurological recovery" was greater for individuals with higher serum albumin concentrations. The association between serum albumin concentrations and neurological outcomes was independent of initial injury severity, treatment with GM-1, and polytrauma. CONCLUSIONS: In spinal cord injury, serum albumin is an independent marker of long-term neurological outcomes. Serum albumin could serve as a feasible biomarker for prognosis at the time of injury and stratification in clinical trials.
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".