Neurological Biomarker Profiles in Royal Canadian Air Force (RCAF) Pilots and Aircrew
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Military aviators can be exposed to extreme physiological stressors, including decompression stress, G-forces, as well as intermittent hypoxia and/or hyperoxia, which may contribute to neurobiological dysfunction/damage. This study aimed to investigate the levels of neurological biomarkers in military aviators to assess the potential risk of long-term brain injury and neurodegeneration. METHODS: This cross-sectional study involved 48 Canadian Armed Forces (CAF) aviators and 48 non-aviator CAF controls. Plasma samples were analyzed for biomarkers of glial activation (GFAP), axonal damage (NF-L, pNF-H), oxidative stress (PRDX-6), and neurodegeneration (T-tau), along with S100b, NSE, and UCHL-1. The biomarker concentrations were quantified using multiplexed immunoassays. RESULTS: < 0.001), indicating increased glial activation, axonal injury, and oxidative stress. Trends toward higher levels of S100b, NSE, and UCHL-1 were observed but were not statistically significant. The elevated biomarker levels suggest cumulative brain damage, raising concerns about potential long-term neurological impairments. CONCLUSIONS: Military aviators are at increased risk for neurobiological injury, including glial and axonal damage, oxidative stress, and early neurodegeneration. These findings emphasize the importance of proactive monitoring and further research to understand the long-term impacts of high-altitude flight on brain health and to develop strategies for mitigating cognitive decline and neurodegenerative risks in this population.
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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 | 0.001 | 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".