A Clinically Relevant Mouse Model of Concussion Incorporating High Rotational Forces
Bibliographic record
Abstract
Clinically relevant models of concussion are critical in understanding the pathophysiology of concussion and its long-term outcomes. To bridge the gap between preclinical and clinical research, animal models of concussion should be produced by mild traumatic brain injuries (mTBIs) that possess the same physical and biomechanical properties found in the mTBIs that cause concussion in humans. Specifically, to have good construct validity the mTBIs used in animal models of concussion should feature closed-head impacts with unrestrained head and body motion, resulting in peak angular velocities that approximate the human experience. We describe a mouse model of concussion using a cortical impactor to deliver closed-head mTBIs. Mice are placed on a break-away platform that allows free head and body movement during and after impact resulting in rapid head rotation. We assessed this model of concussion in over 100 mice carrying humanized versions of the genes encoding the amyloid precursor protein and tau. We found that this method consistently produced injuries with peak angular velocities in mice that, when scaled, approximated the average peak angular velocities reported in concussive football impacts. Face validity of this model of concussion was evaluated by histopathology and revealed that three impacts delivered 24 hours apart led to diffuse axonal injury, astrogliosis, and microglial activation one week after injury, particularly in white matter tracts aligned orthogonally to the axis of rotation. Persistent axonal degeneration was observed up to 6 months postinjury. This mouse model of concussion captures key biomechanical and pathological features of human concussions.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".