Elevated Tau in Military Personnel Relates to Chronic Symptoms Following Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To understand the relationships between traumatic brain injury (TBI), blood biomarkers, and symptoms of posttraumatic stress disorder (PTSD), depression, and postconcussive syndrome symptoms. DESIGN: Cross-sectional cohort study using multivariate analyses. PARTICIPANTS: One hundred nine military personnel and veterans, both with and without a history of TBI. MAIN MEASURES: PTSD Checklist-Civilian Version (PCL-C); Neurobehavioral Symptom Inventory (NSI); Ohio State University TBI Identification Method; Patient Health Questionnaire-9 (PHQ-9); Simoa-measured concentrations of tau, amyloid-beta (Aβ) 40, Aβ42, and neurofilament light (NFL). RESULTS: Controlling for age, sex, time since last injury (TSLI), and antianxiety/depression medication use, NFL was trending toward being significantly elevated in participants who had sustained 3 or more TBIs compared with those who had sustained 1 or 2 TBIs. Within the TBI group, partial correlations that controlled for age, sex, TSLI, and antianxiety/depression medication use showed that tau concentrations were significantly correlated with greater symptom severity, as measured with the NSI, PCL, and PHQ-9. CONCLUSIONS: Elevations in tau are associated with symptom severity after TBI, while NFL levels are elevated in those with a history of repetitive TBIs and in military personnel and veterans. This study shows the utility of measuring biomarkers chronically postinjury. Furthermore, there is a critical need for studies of biomarkers longitudinally 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".