Towards clinical management of traumatic brain injury: a review of models and mechanisms from a biomechanical perspective
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a major worldwide healthcare problem. Despite promising outcomes from many preclinical studies, the failure of several clinical studies to identify effective therapeutic and pharmacological approaches for TBI suggests that methods to improve the translational potential of preclinical studies are highly desirable. Rodent models of TBI are increasingly in demand for preclinical research, particularly for closed head injury (CHI), which mimics the most common type of TBI observed clinically. Although seemingly simple to establish, CHI models are particularly prone to experimental variability. Promisingly, bioengineering-oriented research has advanced our understanding of the nature of the mechanical forces and resulting head and brain motion during TBI. However, many neuroscience-oriented laboratories lack guidance with respect to fundamental biomechanical principles of TBI. Here, we review key historical and current literature that is relevant to the investigation of TBI from clinical, physiological and biomechanical perspectives, and comment on how the current challenges associated with rodent TBI models, particularly those involving CHI, could be improved.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".