Tibial Fracture Exacerbates Traumatic Brain Injury Outcomes and Neuroinflammation in a Novel Mouse Model of Multitrauma
Bibliographic record
Abstract
Multitrauma is a common medical problem worldwide, and often involves concurrent traumatic brain injury (TBI) and bone fracture. Despite the high incidence of combined TBI and fracture, preclinical TBI research commonly employs independent injury models that fail to incorporate the pathophysiologic interactions occurring in multitrauma. Here, we developed a novel mouse model of multitrauma, and investigated whether bone fracture worsened TBI outcomes. Male mice were assigned into four groups: sham-TBI+sham-fracture (SHAM); sham-TBI+fracture (FX); TBI+sham-fracture (TBI); and TBI+fracture (MULTI). The injury methods included a closed-skull weight-drop TBI model and a closed tibial fracture. After a 35-day recovery, mice underwent behavioral testing and magnetic resonance imaging (MRI). MULTI mice displayed abnormal behaviors in the open-field compared with all other groups. On MRI, MULTI mice had enlarged ventricles and diffusion abnormalities compared with all other groups. These changes occurred in the presence of heightened neuroinflammation in MULTI mice at 24 hours and 35 days after injury, and elevated edema and blood-brain barrier disruption at 24 hours after injury. Together, these findings indicate that tibial fracture worsens TBI outcomes, and that exacerbated neuroinflammation may be an important factor that contributes to these effects, which warrants further investigation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".