Internal Jugular Vein Compression Mitigates Traumatic Axonal Injury in a Rat Model by Reducing the Intracranial Slosh Effect
Bibliographic record
Abstract
BACKGROUND: Traumatic brain injury (TBI) remains a devastating condition for which extracranial protection traditionally has been in the form of helmets, which largely fail to protect against intracranial injury. OBJECTIVE: To determine whether the pathological outcome after traumatic brain injury can be improved via slosh mitigation by internal jugular vein (IJV) compression. METHODS: Two groups of 10 adult male Sprague-Dawley rats were subjected to impact-acceleration traumatic brain injury. One group underwent IJV compression via application of a collar before injury; the second group did not. Intracranial pressure and intraocular pressure were measured before and after IJV compression to assess collar performance. All rats were killed after a 7-day recovery period, and brainstem white matter tracts underwent fluorescent immunohistochemical processing and labeling of β-amyloid precursor protein, a marker of axonal injury. Digital imaging and statistical analyses were used to determine whether IJV compression resulted in a diminished number of injured axons. RESULTS: Compression of the IJV resulted in an immediate 30% increase in intraocular and intracranial pressures. Most notably, IJV compression resulted in > 80% reduction in the number of amyloid precursor protein-positive axons as indicated by immunohistochemical analysis. CONCLUSION: Using a standard acceleration-deceleration laboratory model of mild traumatic brain injury, we have shown successful prevention of axonal injury after IJV compression as indicated by immunohistochemical staining of amyloid precursor protein. We argue that IJV compression reduces slosh-mediated brain injury by increasing intracranial blood volume, which can be indirectly measured by intracranial and intraocular pressures.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".