Effect of slosh mitigation on histologic markers of traumatic brain injury
Bibliographic record
Abstract
OBJECT: Helmets successfully prevent most cranial fractures and skull traumas, but traumatic brain injury (TBI) and concussions continue to occur with frightening frequency despite the widespread use of helmets on the athletic field and battlefield. Protection against such injury is needed. The object of this study was to determine if slosh mitigation reduces neural degeneration, gliosis, and neuroinflammation. METHODS: Two groups of 10 adult male Sprague-Dawley rats were subjected to impact-acceleration TBI. One group of animals was fitted with a collar inducing internal jugular vein (IJV) compression prior to injury, whereas the second group received no such collar prior to injury. All rats were killed 7 days postinjury, and the brains were fixed and embedded in paraffin. Tissue sections were processed and stained for markers of neural degeneration (Fluoro-Jade B), gliosis (glial fibrillary acidic protein), and neuroinflammation (ionized calcium binding adapter molecule 1). RESULTS: Compared with the controls, animals that had undergone IJV compression had a 48.7%-59.1% reduction in degenerative neurons, a 36.8%-45.7% decrease in reactive astrocytes, and a 44.1%-65.3% reduction in microglial activation. CONCLUSIONS: The authors concluded that IJV compression, a form of slosh mitigation, markedly reduces markers of neurological injury in a common model of TBI. Based on findings in this and other studies, slosh mitigation may have potential for preventing TBI in the clinical population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".