Blood Biomarkers Predict Future Cognitive Decline after Military-RelatedTraumatic Brain Injury
Bibliographic record
Abstract
Background:: Traumatic brain injury (TBI) has been associated with increased likelihood of late-life dementia; however, the mechanisms driving this relationship are elusive. Blood-based biomarkers may provide insight into these mechanisms and serve as useful prognostic indicators of cognitive recovery or decline following a TBI. Objective: The aim of this study was to examine blood biomarkers within one year of TBI and explore their relationship with cognitive decline. Methods: Service members and veterans (n=224) without injury (n=77), or with history of bodily injury (n=37), uncomplicated mild TBI (n=55), or more severe TBI (n=55), underwent a blood draw and neuropsychological assessment within one year of their injury as part of a case-control study. A subsample (n=87) completed follow-up cognitive assessment. Results: In the more severe TBI group, baseline glial fibrillary acidic protein (p=.008) and ubiquitin C-terminal hydrolase-L1 (p=.026) were associated with processing speed at baseline, and baseline ubiquitin C-terminal hydrolase-L1 predicted change in immediate (R2Δ=.244, p=.005) and delayed memory (R2Δ=.390, p=.003) over time. In the mild TBI group, higher baseline tau predicted greater negative change in perceptual reasoning (R2Δ=.188, p=.033) and executive functioning (R2Δ=.298, p=.007); higher baseline neurofilament light predicted greater negative change in perceptual reasoning (R2Δ=.211, p=.012). Conclusion: Baseline ubiquitin C-terminal hydrolase-L1 strongly predicted memory decline in the more severe TBI group, while tau and neurofilament light strongly predicted decline in the mild TBI group. A panel including these biomarkers could be particularly helpful in identifying those at risk for future cognitive decline following TBI.
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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 | 0.003 |
| 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 | 0.001 |
| 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".