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Record W2117893550 · doi:10.1161/strokeaha.110.594457

Rodent Models of Intracerebral Hemorrhage

2010· review· en· W2117893550 on OpenAlexafffund
Crystal L. MacLellan, Gergely Silasi, Angela M. Auriat, Frederick Colbourne

Bibliographic record

VenueStroke · 2010
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineIntracerebral hemorrhageIntensive care medicineBench to bedsideClinical trialNeuroprotectionRodent modelStroke (engine)Predictive valuePathologySurgeryPharmacologyInternal medicineMedical physicsSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

The collagenase and whole blood intracerebral hemorrhage (ICH) models are widely used to identify mechanisms of injury and to evaluate treatments. Despite preclinical successes, to date, no treatment tested in phase III clinical trials has benefited ICH patients. These failures call into question the predictive value of current ICH models. By highlighting differences between these common rodent models of ICH, we sought to help investigators choose the more appropriate model for their study and to encourage the use of both whenever possible. For instance, we previously reported substantial differences in the bleeding profile, progression of cell death, and functional outcome between these models. These and other differences influence the efficacy and mechanisms of action of various treatment modalities. Thus, in this review, we also summarize neuroprotective and rehabilitation findings in each model. We conclude that differences between ICH models along with our current inability to identify the more clinically predictive model necessitate that preclinical assessments should normally be done in both. Such an approach, coupled with better assessment practices, will likely improve chances of future clinical success.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.345
Teacher spread0.293 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations113
Published2010
Admission routes2
Has abstractyes

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