Blast effects on post-concussive and mental health outcomes: Data from Canadian Armed Forces breachers and snipers
Bibliographic record
Abstract
LAY SUMMARY There has been increasing interest in understanding the impact of blast exposure on health and performance in military members and Veterans. This phenomenon has proven difficult to study because personnel diagnosed with blast-induced mild traumatic brain injury (mTBI) typically also exhibit emotional difficulties such as posttraumatic stress disorder (PTSD), likely because the events that led to mTBI in theatre were also emotionally traumatic. In turn, this comorbidity makes it difficult to tease apart symptoms uniquely due to blast-induced mTBI or PTSD. Researchers have therefore explored surrogate settings wherein the effects of blast exposure can be assessed in an operationally realistic, yet scientifically more controlled manner, such as breacher and sniper training. To that aim, researchers administered a measure of post-concussive symptomatology and two mental health scales to breachers and snipers, as well as sex- and age-matched military controls. The breachers and snipers reported greater levels of post-concussive symptoms, but not poorer mental health outcomes. Therefore, post-concussive symptoms and mental health outcomes might be dissociable when the impact of repetitive exposure to low-level blast is assessed in the context of training and operations, rather than following warzone deployment involving primarily high-level blast exposure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".