Brain Response to Overpressure and Recoil Loads from Discharge of Long-Range Precision Rifle
Bibliographic record
Abstract
The presence of concussion-like symptoms related to overpressure exposure and recoil forces from long-range precision rifle (LPR) training has been reported in the literature. However, the recoil head kinematics, overpressure loadings from LPR discharge, and the interaction of the two load paths have not been previously quantified. \nIn the present study, experiments were undertaken by the Defense Research and Development Canada (DRDC) Valcartier Research Centre, using an instrumented head form to measure the overpressure from LPR discharges and to measure head kinematics resulting from recoil using instrumented mouthguards on human volunteers. The measurements included a high-speed video to enable estimation of the relative onset timings of overpressure and recoil head kinematics. The LPR configurations encompassed both muzzle suppressor and non-suppressor configurations. Then, planar finite element (FE) head models (in the sagittal and transverse planes) were used to quantify the effects of the measured loadings on the brain response. The models were used to simulate three boundary conditions: only the overpressure, only the recoil head kinematics, and combining the two loadings to investigate the interaction of the load paths. \nThe overpressure resulting from discharge of the LPR was reduced significantly when the suppressor configuration was employed. The overpressure reached the head 3.6 ms after exiting the barrel of the LPR, with peaks ranging from 0.2 to 27.6 kPa with and without suppressor, respectively. The onset of recoil head kinematics varied between operators, occurring between 7.4 to 24.4 ms after the onset of overpressure loading to the head. \nIn addition, the FE models showed that the intracranial pressure response predicted in the head demonstrated an interaction between overpressure and head kinematics, while strain in the brain was largely governed by recoil head kinematics. \nThe results of this study provide important information regarding the relative severities and interaction between the overpressure and recoil head kinematics in LPR operators.
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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 | 0.000 | 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) | 0.001 | 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".