The impact of repetitive exposure to low-level blast on neurocognitive function in Canadian Armed Forces’ breachers, snipers, and military controls
Bibliographic record
Abstract
Abstract Objective: The primary aim of this study was to evaluate whether military occupations with repetitive exposure to low-level blast (i.e., breachers and snipers) display poorer neurocognitive status compared to military controls without prior occupational engagement as breachers and/or snipers, and whether that effect is mediated by self-reported mental health symptoms. Method: With data collected from Canadian Armed Forces (CAF) breachers and snipers and sex- and age-matched CAF controls ( n = 112), mental health was assessed using the PCL-5 (PTSD) and the Brief Symptoms Inventory , and neurocognitive function based on a set of computerized tasks (i.e., four-choice reaction time task, delayed matching-to-sample, n-back, Stroop). Directed Acyclic Graphs (DAGs) were created to establish a causal framework describing the potential effect of occupation on neurocognitive function while considering mental health. Factor analysis modeling was used to establish the latent construct of neurocognitive function, which was then incorporated into student- t models for effect estimation, following assumptions derived from causal inference principles. Results: Our results demonstrated that it is snipers specifically who displayed lower neurocognitive performance compared to breachers and controls. Critically, this effect was not mediated by mental health status. In fact, mental health was generally better in both breachers and snipers when compared to controls. Conclusions: When the focus is on occupations with repetitive exposure to low-level blast, the snipers in particular are impacted most in terms of neurocognitive function. We speculate that this might be due to additional impact of recoil forces exacerbating the effect of blast overpressure on the nervous system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".