A translational perspective on intracranial pressure responses following intracerebral hemorrhage in animal models
Bibliographic record
Abstract
As with ischemic and traumatic brain injury, raised intracranial pressure (ICP) is a common life-threatening complication of intracerebral hemorrhage (ICH). Elevated ICP can lead to cerebral ischemia, impaired cerebrospinal fluid flow, and brain herniation. Compliance mechanisms accommodate to mitigate raised ICP, but these mechanisms are often overwhelmed after large ICH, and thus treatments are needed to lower ICP. Rodents, canines, and non-human primates have been used to model ICH and study both the consequences of and treatments for high ICP. However, the methods used to study ICP can be expensive and technically challenging, leading to a scarcity of research in the area. Similarly, replication among labs is difficult owing to differences in ICP devices, measurement location, and data analysis. Many treatments to lower ICP have been investigated, some of which are currently used in clinical practice, such as surgical interventions and osmotic therapies, but strong evidence is often lacking. Here we review the mechanistic importance of raised ICP in determining poor outcome after ICH, and specifically the difficulties of accurately modeling and measuring ICP in preclinical research, and how this might affect research translation.
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".