A Large Animal Model of Non-Penetrating Impact Induced Head Injury: Apparatus, Head Kinematics and Impact Mechanics
Bibliographic record
Abstract
Large animal models are important tools for investigating traumatic brain injury (TBI) mechanics and pathophysiology. Ideal TBI models have repeatable and well characterized mechanics and produce injury via clinically relevant mechanisms. In this study, a custom instrumented impact apparatus has been developed and used to impact the heads of deceased sheep at three impact energies to characterise the impact mechanics and resulting head kinematics. Thirty-eight impacts were performed on 10 deceased sheep to assess controllability, inter- and intra-animal repeatability of head kinematics, and impact mechanics. Four animals received single impacts followed by computed tomography of the cranium; no depressed skull fractures were observed. Injury device impact velocity was 9.2±0.6 m/s, 12.6±0.7 m/s, and 15.7±0.5 m/s, at the three energies, respectively, with coefficient of variation (CoV) of 3.1%. Peak force increased linearly with impact energy (p<0.001), from 20.3±4 kN for the lowest energy impacts to 33.4±6.7 kN at the highest energy. Peak linear acceleration, angular acceleration, and angular velocity of the head were measured with head-mounted accelerometers and increased linearly with impact energy (p<0.05), with the highest energy impacts resulting in peak resultant angular acceleration and velocity of 240.5±51.4 krad/s2, and 73.6±12.1 rad/s, respectively. Repeatability of angular head kinematics increased with increasing impact energy, with CoVs comparable to kinematics of other TBI animal models. The outcomes of this study will be used to inform the design of future in vivo animal studies using this injury apparatus and repeatable test protocol.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".