An Automated Kinematic Measurement System for Sagittal Plane Murine Head Impacts
Bibliographic record
Abstract
Mild traumatic brain injuries are typically caused by nonpenetrating head impacts that accelerate the skull and result in deformation of the brain within the skull. The shear and compressive strains caused by these deformations damage neural and vascular structures and impair their function. Accurate head acceleration measurements are necessary to define the nature of the insult to the brain. A novel murine head tracking system was developed to improve the accuracy and efficiency of kinematic measurements obtained with high-speed videography. A three-dimensional (3D)-printed marker carrier was designed for rigid fixation to the upper jaw and incisors with an elastic strap around the snout. The system was evaluated by impacting cadaveric mice with the closed head impact model of engineered rotational acceleration (CHIMERA) system using an energy of 0.7 J (5.29 m/s). We compared the performance of the head-marker system to the previously used skin-tracking method and documented significant improvements in measurement repeatability (aggregate coefficient of variation (CV) within raters from 15.8 to 1.5 and between raters from 15.5 to 1.5), agreement (aggregate percentage error from 24.9 to 8.7), and temporal response (aggregate temporal curve agreement from 0.668 to 0.941). Additionally, the new system allows for automated software tracking, which dramatically decreases the analysis time required (74% reduction). This novel head tracking system for mice offers an efficient, reliable, and real-time method to measure head kinematics during high-speed impacts using CHIMERA or other rodent or small mammal head impact models.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".