Brainstem and Cortical Spreading Depolarization in a Closed Head Injury Rat Model
Bibliographic record
Abstract
Traumatic brain injury (TBI) is the leading cause of death in young individuals, and is a major health concern that often leads to long-lasting complications. However, the electrophysiological events that occur immediately after traumatic brain injury, and may underlie impact outcomes, have not been fully elucidated. To investigate the electrophysiological events that immediately follow traumatic brain injury, a weight-drop model of traumatic brain injury was used in rats pre-implanted with epidural and intracerebral electrodes. Electrophysiological (near-direct current) recordings and simultaneous alternating current recordings of brain activity were started within seconds following impact. Cortical spreading depolarization (SD) and SD-induced spreading depression occurred in approximately 50% of mild and severe impacts. SD was recorded within three minutes after injury in either one or both brain hemispheres. Electrographic seizures were rare. While both TBI- and electrically induced SDs resulted in elevated oxidative stress, TBI-exposed brains showed a reduced antioxidant defense. In severe TBI, brainstem SD could be recorded in addition to cortical SD, but this did not lead to the death of the animals. Severe impact, however, led to immediate death in 24% of animals, and was electrocorticographically characterized by non-spreading depression (NSD) of activity followed by terminal SD in both cortex and brainstem.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".