Relationship between edema and intracranial pressure following intracerebral hemorrhage in rat
Bibliographic record
Abstract
Elevated intracranial pressure (ICP) is a potentially fatal consequence of intracerebral hemorrhage (ICH). As the mass of the hematoma and regional edema builds, ICP rises and becomes increasingly variable acutely after stroke. High ICP may worsen cellular injury and edema by impairing local tissue perfusion, fueling a cycle that may ultimately cause fatality through ischemia and brain herniation. Time spent above an ICP of 20 mmHg often predicts a greater risk of death and disability following ICH. Compensatory mechanisms combat rising ICP. Classically, these include cerebrospinal fluid volume loss and cerebrovascular autoregulation, such as a reduction in the volume of venous blood. Additional mechanisms such as brain tissue compliance and skull volume compensation may also contribute. Compensatory compliance mechanisms are limited, and they vary by age and many other factors. Animal models of ICH are widely used to assess these variables and to gauge putative therapeutics. Most often those studies rely upon simple measures of edema, which may not accurately predict ICP data. Thus, we analyzed our past studies characterizing ICP, edema, and tissue compliance responses to striatal ICH in rat, including the collagenase (C-ICH) and whole blood models (WB-ICH). We found that both ICH models raised ICP, with greater effects in the C-ICH model, which may thus better reflect clinical findings of concern. Importantly, measures of edema, such as in the damaged hemisphere, on their own are not predictive of average or peak ICP response within either model, unless assessing across a very wide range of injury severities, or when including non-stroke animals. We caution against using edema data as a surrogate measure of mass effect and ICP following ICH.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".