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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".