Association of Marshall CT Scores with GFAP, UCH-L1, Tau, NfL, and p-Tau231 After Traumatic Brain Injury
Bibliographic record
Abstract
This study identifies a significant association among blood-based biomarkers of traumatic brain injury (TBI) and the Marshall CT classification of TBI (MCTC) scores, but not with Glasgow Coma Scale (GCS) scores. We aimed to determine whether GCS and MCTC scores relate to glial fibrillary acid protein (GFAP), ubiquitin carboxy hydrolase-1 (UCH-L1), tau, neurofilament light chain (NfL), and phosphorylated tau (p-tau231) concentrations following acute TBIs. Participants included patients from 20 trauma centers across 12 regional sites in the United States and Canada with an initial CT scan within 6 h after TBI and GCS scores of 3 to 12. Blood samples collected upon hospital arrival were analyzed for biomarker concentrations (pg/mL). Concentrations from 271 patients with GCS ≥ 9 were compared to 145 with GCS ≤ 9. Samples from 347 patients with MCTC < 3 were compared to 70 with MCTC ≥ 3. No significant differences in GCS groups were found (p’s > 0.5), while MCTC groups differed significantly (p’s < 0.001). Higher concentrations of plasma GFAP, NfL, and p-tau231 correlated with MCTC scores > 3, with no associations with GCS. Future research might show an application in individual risk assessments to improve triaging of TBI 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 | 0.000 | 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 | 0.000 | 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".