Routine Blood Chemistry Predicts Functional Recovery After Traumatic Spinal Cord Injury: A Post Hoc Analysis
Bibliographic record
Abstract
BACKGROUND: Spinal cord injury (SCI) leads to various degrees of lifelong functional deficits. Most individuals with incomplete SCI experience a certain degree of functional recovery, especially within the first-year postinjury. However, this is difficult to predict, and surrogate biomarkers are urgently needed. OBJECTIVE: We aimed to (1) determine if routine blood chemistry parameters are related to neurological recovery after SCI, (2) evaluate if such parameters could predict functional recovery, and (3) establish cutoff values that could inform clinical decision-making. METHODS: We performed a post hoc analysis of routine blood chemistry parameters in patients with traumatic SCI (n = 676). Blood samples were collected between 24 and 72 hours as well as at 1, 2, 4, 8, and 52 weeks postinjury. Linear mixed models, regression analysis, and unbiased recursive partitioning (URP) of blood chemistry data were used to relate to and predict walking recovery 1 year postinjury. RESULTS: The temporal profile of platelet counts and serum levels of albumin, alkaline phosphatase, and creatinine differentiated patients who recovered walking from those who remained wheelchair bound. The 4 blood chemistry parameters from the sample collection 8 weeks postinjury predicted functional recovery observed 1 year after incomplete SCI. Finally, URP defined a cutoff for serum albumin at 3.7 g/dL, which in combination with baseline injury severity differentiates individuals who regain ambulation from those not able to walk. Specifically, about 80% of those with albumin >3.7 g/dL recovered walking. CONCLUSIONS: Routine blood chemistry data from the postacute phase, together with baseline injury severity, predict functional outcome after incomplete 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".