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Record W2341285926 · doi:10.1080/01616412.2016.1144671

Animal models of spontaneous intracerebral hemorrhage

2016· review· en· W2341285926 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurological Research · 2016
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineIntracerebral hemorrhageAnimal modelNitric oxide synthaseStroke (engine)Blood pressureDiseaseCerebral amyloid angiopathyNitric oxideRenin–angiotensin systemGenetically modified mousePharmacologyTransgeneInternal medicineBiologyBiochemistrySubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.211
GPT teacher head0.442
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it