Blood Biomarkers Relate to Cognitive Performance Years after Traumatic Brain Injury in Service Members and Veterans
Bibliographic record
Abstract
OBJECTIVE: This study examines the relationship of serum total tau, neurofilament light (NFL), ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), and glial fibrillary acidic protein (GFAP) with neurocognitive performance in service members and veterans with a history of traumatic brain injury (TBI). METHOD: Service members (n = 488) with a history of uncomplicated mild (n = 172), complicated mild, moderate, severe, or penetrating TBI (sTBI; n = 126), injured controls (n = 116), and non-injured controls (n = 74) prospectively enrolled from Military Treatment Facilities. Participants completed a blood draw and neuropsychological assessment a year or more post-injury. Six neuropsychological composite scores and presence/absence of mild neurocognitive disorder (MNCD) were evaluated. Within each group, stepwise hierarchical regression models were conducted. RESULTS: Within the sTBI group, increased serum UCH-L1 was related to worse immediate memory and delayed memory (R2Δ = .065-.084, ps < .05) performance, while increased GFAP was related to worse perceptual reasoning (R2Δ = .030, p = .036). Unexpectedly, within injured controls, UCH-L1 and GFAP were inversely related to working memory (R2Δ = .052-.071, ps < .05), and NFL was related to executive functioning (R2Δ = .039, p = .021) and MNCD (Exp(B) = 1.119, p = .029). CONCLUSIONS: Results suggest GFAP and UCH-L1 could play a role in predicting poor cognitive outcome following complicated mild and more severe TBI. Further investigation of blood biomarkers and cognition is warranted.
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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".