Clinical Relevance of Behavior Testing in Animal Models of Traumatic Brain Injury
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a leading cause of morbidity worldwide, with patients often suffering from consequences such as cognitive deficits, social abnormalities, anxiety, depression, pain, and motor dysfunction. Given that these impairments often have a significant impact on the patient's quality of life, a key aim of therapeutic intervention in TBI is to mitigate these effects. Translational strategies to develop such interventions have heavily featured animal models of TBI. To assess the efficacy of interventions in these models, a range of behavioral outcomes are utilized. However, in light of the past translational failures that have plagued the TBI field, the clinical relevance of these preclinical behavioral tests is now being scrutinized. This article will summarize the behavioral consequences of TBI in humans; describe common methods available for testing cognition, social function, motor ability, pain, as well as depression- and anxiety-like behaviors in animal models of TBI; provide an overview of the results from TBI animal model studies that have utilized these methods; and discuss these pre-clinical behavior methods and findings in terms of their relevance to the clinical TBI setting. We conclude that there is translational value in these methods and their related findings, but also suggest strategies and future research to improve the clinical relevance of behavior testing in animal models of TBI.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".