Animal models of spontaneous intracerebral hemorrhage
Bibliographic record
Abstract
OBJECTIVES: Intracerebral hemorrhage (ICH) is a type of stroke that results in significant mortality and morbidity. Currently there is no definitive treatment for this disease. The paucity of animal models that reflect the heterogeneity of this spontaneous human disease could be the reason. METHODS: In this review, we searched the literature for animal models of spontaneous ICH and found eight relevant papers. RESULTS: Two were related to hypertension and six were related to cerebral amyloid angiopathy (CAA). One model used double transgenic mice overexpressing human renin and angiotensinogen which caused the mice to be hypertensive. Induction of ICH, however required addition of a high salt diet and nitric oxide synthase inhibition. Another mouse model of hypertension employed subcutaneous angiotensin II infusion and nitric oxide synthase inhibition plus acute injections of angiotensin to further elevate blood pressure. Five CAA models were in transgenic mice overexpressing amyloid precursor protein. One relied on the natural development of CAA in squirrel monkeys. CONCLUSIONS: While all of the spontaneous ICH models have some advantages, the disadvantages include the sporadic time of onset of ICH and variability in size and location of ICH. Since there are no known efficacious treatments for ICH, it is not known if findings in the animal models will find treatments that are effective in humans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".