Characterization of Sleep, Emotional, and Cognitive Functions in a New Rat Model of Concomitant Spinal Cord and Traumatic Brain Injuries
Bibliographic record
Abstract
Traumatic injuries to the spinal cord or the brain have serious medical consequences and lead to long-term disability. The epidemiology, medical complications, and prognosis of isolated spinal cord injury (SCI) and traumatic brain injury (TBI) have been well described. However, there are limited data on patients suffering from concurrent SCI and TBI, even if a large proportion of SCI patients have concomitant TBI. The complications associated with this "dual-diagnosis" such as cognitive or behavioral dysfunction are well known in the rehabilitation setting, but evidence-based and standardized approaches for diagnosis and treatment are lacking. Our goal was to develop and characterize a pre-clinical animal model of concurrent SCI and TBI to help identifying "dual-diagnosis" tools. Female rats received a unilateral contusive SCI at the thoracic level alone (SCI group) or combined with a TBI centered on the contralateral sensorimotor cortex (SCI-TBI group). We first validated that the SCI extent was comparable between SCI-TBI and SCI groups, and that hindlimb function was impaired. We characterized various neurological outcomes, including locomotion, sleep architecture, brain activity during sleep, depressive- and anxiety-like behaviors, and working memory. We report that SCI-TBI and SCI groups show similar impairments in global locomotor function. While wake/sleep amount and distribution and anxiety- and depression-like symptoms were not affected in SCI-TBI and SCI groups in comparison to the control group (laminectomy and craniotomy only), working memory was impaired only in SCI-TBI rats. This pre-clinical model of concomitant SCI and TBI, including more severe variations of it, shows a translational value for the identification of biomarkers to refine the "dual-diagnosis" of neurotrauma in humans.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".