INJURY RISK TO THE UPPER EXTREMITY RESULTING FROM BEHIND SHIELD BLUNT TRAUMA
Bibliographic record
Abstract
Ballistic shields are supported by a user’s arm, placing the upper extremity at close proximity to the back-face of the shield. Although ballistic shields must pass a protective standard that outlines projectile (bullet) penetration; there is no standard that stipulates the amount of acceptable deformation when ballistic shields stop or deflect projectiles. There are no injury criteria developed for the high-rate, short duration and focal loading that is typical of shield back-face deformation from these events. In this research, an anthropomorphic test device (ATD) was modified to allow for additional instrumentation capable of measuring these loads. It was then used in a ballistic testing facility to quantify loading at the hand, wrist, forearm, and elbow. A lightweight projectile was created that matched the shape and stiffness of the deforming ballistic shield and impacts within 5% of the peak force measured in the ballistic testing facility were applied with it to post-mortem human subjects (PMHS) until failure. Eight 50th percentile male PMHS pairs were segmented at the mid-humerus and impacted to failure to determine the fracture threshold of the hand, wrist, forearm, and elbow, confirmed by x-ray imaging. The peak force required to generate fracture varied significantly among anatomical location, indicating boundary conditions influence failure threshold. Further, these injury criteria were substantially different than previously reported criteria for other loading events (e.g., automotive), highlighting the importance of developing injury criteria specific for the intended application. An existing finite element human body model designed for automotive impacts was also assessed for its applicability to predict injury in these high-rate loading scenarios, and performed well for peak force, but not for the force-time curve shape. This is the first study of its kind to assess injury risk resulting from shield behind armour blunt trauma, and results from this work will inform a protective standard to assess ballistic shields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".