Prognostic Value of Serum Biomarkers in Patients With Moderate-Severe Traumatic Brain Injury, Differentiated by Marshall Computer Tomography Classification
Bibliographic record
Abstract
Prognostication is challenging in patients with traumatic brain injury (TBI) in whom computed tomography (CT) fails to fully explain a low level of consciousness. Serum biomarkers reflect the extent of structural damage in a different way than CT does, but it is unclear whether biomarkers provide additional prognostic value across the range of CT abnormalities. This study aimed to determine the added predictive value of biomarkers, differentiated by imaging severity. This prognostic study used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study (2014–2017). The analysis included patients aged ≥16 years with a moderate-severe TBI (Glasgow Coma Scale [GCS] <13) who had an acute CT and serum biomarkers obtained ≤24h of injury. Of six protein biomarkers (GFAP, NFL, NSE, S100B, Tau, UCH-L1), the most prognostic panel was selected using lasso regression. The performance of established prognostic models (CRASH and IMPACT) was assessed before and after the addition of the biomarker panel and compared between patients with different CT Marshall scores (Marshall score <3 vs. Marshall score ≥3). Outcome was assessed at six months post-injury using the extended Glasgow Outcome Scale (GOSE), and dichotomized into favorable and unfavorable (GOSE <5). We included 872 patients with moderate-severe TBI. The mean age was 47 years (range 16–95); 647 (74%) were male and 438 (50%) had a Marshall CT score <3. The serum biomarkers GFAP, NFL, S100B and UCH-L1 provided complementary prognostic information; NSE and Tau showed no added value. The addition of the biomarker panel to established prognostic models increased the area under the curve (AUC) by 0.08 and 0.03, and the explained variation in outcome by 13–14% and 7–8%, for patients with a Marshall score of <3 and ≥3, respectively. The incremental AUC of biomarkers for individual models was significantly greater when the Marshall score was <3 compared with ≥3 ( p < 0.001). Serum biomarkers improve outcome prediction after moderate-severe TBI across the range of imaging severities and especially in patients with a Marshall score <3.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".