Neuroinflammation as a Target for Intervention in Subarachnoid Hemorrhage
Bibliographic record
Abstract
Aneurysmal subarachnoid hemorrhage (SAH) is a sub-type of hemorrhagic stroke associated with the highest rates of mortality and long-term neurological disabilities. Despite the improvement in the management of SAH patients and the reduction in case fatality in the last decades, disability and mortality remain high in this population. Brain injury can occur immediately and in the first days after SAH. This early brain injury can be due to physical effects on the brain such as increased intracranial pressure, herniations, intracerebral, intraventricular hemorrhage, and hydrocephalus. After the first 3 days, angiographic cerebral vasospasm (ACV) is a common neurological complication that in severe cases can lead to delayed cerebral ischemia and cerebral infarction. Consequently, the prevention and treatment of ACV continue to be a major goal. However, most treatments for ACV are vasodilators since ACV is due to arterial vasoconstriction. Other targets also have included those directed at the underlying biochemical mechanisms of brain injury such as inflammation and either independently or as a consequence, cerebral microthrombosis, cortical spreading ischemia, blood-brain barrier breakdown, and cerebral ischemia. Unfortunately, no pharmacologic treatment directed at these processes has yet shown efficacy in SAH. Enteral nimodipine and the endovascular treatment of the culprit aneurysm, remain the only treatment options supported by evidence from randomized clinical trials to improve patients' outcome. Currently, there is no intervention directly developed and approved to target neuroinflammation after SAH. The goal of this review is to provide an overview on anti-inflammatory drugs tested after aneurysmal SAH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".