Imaging of Neurotrauma in Acute and Chronic Settings
Bibliographic record
Abstract
Traumatic injuries of the brain and spinal cord are a significant source of mortality and long-term disability. A recent systematic study in a rat model of spinal cord injury (SCI) indicates severe, destructive, and very protracted inflammation as the key mechanism initiated by the massive injury involving the white matter. Although the severe inflammation is localized and counteracted by astrogliosis, it has a damaging effect on the blood vessels in the surrounding spinal cord, leading to persistent vasogenic edema. Evaluation of these injuries with imaging of the brain and spinal cord plays a crucial role in the acute trauma work-up, allowing clinicians to quickly identify abnormalities that require immediate medical or surgical intervention or to exclude them from the workup. Recently, anti-inflammatory agents have been shown to inhibit and accelerate the elimination of post-SCI inflammation in preclinical studies, and an exciting potential has arisen for the use of antiinflammatory drugs in clinical studies to achieve neuroprotection (i.e., inhibition of destruction caused by inflammation) and to inhibit vasogenic edema in SCI, traumatic brain injury, and stroke. In both subacute and chronic settings, imaging can guide therapy and provide important prognostic information. In this review, we discuss the imaging workup and evolving imaging findings of neurotrauma in the acute and chronic setting, including conventional and advanced imaging techniques. As neuroimaging is the primary mode of diagnostic analysis in neurotrauma, it is a critical component in future clinical trials evaluating neuroprotective therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".