[P1–389]: TAU, Aβ‐AMYLOID, AND COGNITIVE FUNCTION FOLLOWING SERVICE‐RELATED TRAUMATIC BRAIN INJURY IN VIETNAM WAR VETERANS
Bibliographic record
Abstract
Often labelled the “signature wound” of modern day warfare, Traumatic Brain Injury (TBI) has been diagnosed in over 355,000 US military service personnel since 2000. Epidemiological research indicates that veterans with TBI are 2–4 times more likely to develop dementia than controls; however, mechanisms contributing to this relationship are poorly understood. The aim of this study was to investigate if Vietnam war veterans without mild cognitive impairment or dementia, but with TBI show evidence of Alzheimer's disease (AD) pathological markers, as assessed by Aβ-amyloid, tau and glucose metabolism using PET, alongside neuropsychological testing. Fifty-five male participants - 29 veterans with TBI (aged 68.1±2.25 years) and 24 veteran controls (aged 69.6±5.29 years) - underwent neuropsychological assessment, FDG, tau (F-AV1451) and Ab-amyloid PET (F-Florbetaben). Standardized Uptake Value Ratios (SUVR) for all PET tracers were calculated using the cerebellar cortex as reference region. Analyses were adjusted for scores on the Geriatric Depression Scale and Clinician-Administered PTSD Scale. The TBI group performed significantly worse than controls on a number of measures across cognitive domains: WMS Logical Memory I & II (10.24 ±3.78 vs 14.33 ± 3.91, p<0.001; 8.48 ± 3.90 vs 11.63 ± 4.89, p=0.01), MoCA (25.59 ± 2.24 vs 28.12 ± 1.85, p<0.001) and MMSE (27.76 ± 1.70 vs 28.67 ± 0.96, p=0.02). However, the TBI cohort did not differ significantly from controls in F-AV1451 (1.17 ± 0.09 vs 1.15 ± 0.08, p=0.29), F-Florbetaben (1.34 ± 0.20 vs 1.27 ± 0.20, p=0.23) nor FDG tracer retention (1.08 ± 0.09 vs 1.08 ± 0.07, p=0.95). These preliminary findings suggest that whilst TBI is associated with later-life cognitive deficits, these deficits are not associated with AD pathology. Further study is needed to confirm our observations and to determine if direct brain injury can be detected to explain these later life deficits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".