Extracellular vesicle neurofilament light is elevated within the first 12-months following traumatic brain injury in a U.S military population
Bibliographic record
Abstract
Abstract Traumatic brain injury (TBI) can be associated with long-term neurobehavioral symptoms. Here, we examined levels of neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in extracellular vesicles isolated from blood, and their relationship with TBI severity and neurobehavioral symptom reporting. Participants were 218 service members and veterans who sustained uncomplicated mild TBIs (mTBI, n = 107); complicated mild, moderate, or severe TBIs (smcTBI, n = 66); or Injured controls (IC, orthopedic injury without TBI, n = 45). Within one year after injury, but not after, NfL was higher in the smcTBI group than mTBI (p = 0.001, d = 0.66) and IC (p = 0.001, d = 0.35) groups, which remained after controlling for demographics and injury characteristics. NfL also discriminated the smcTBI group from IC (AUC:77.5%, p < 0.001) and mTBI (AUC:76.1%, p < 0.001) groups. No other group differences were observed for NfL or GFAP at either timepoint. NfL correlated with post-concussion symptoms (rs = − 0.38, p = 0.04) in the mTBI group, and with PTSD symptoms in mTBI (rs = − 0.43, p = 0.021) and smcTBI groups (rs = − 0.40, p = 0.024) within one year after injury, which was not confirmed in regression models. Our results suggest the potential of NfL, a protein previously linked to axonal damage, as a diagnostic biomarker that distinguishes TBI severity within the first year after injury.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".