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Record W3200828984 · doi:10.3390/cells10102513

Central Nervous System Tissue Regeneration after Intracerebral Hemorrhage: The Next Frontier

2021· review· en· W3200828984 on OpenAlexafffund
Ruiyi Zhang, Mengzhou Xue, V. Wee Yong

Bibliographic record

VenueCells · 2021
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsRegeneration (biology)Intracerebral hemorrhageNeuroscienceMedicineCentral nervous systemMicrogliaStroke (engine)Regenerative medicineBrain tissuePathologyBiologySurgeryStem cellInflammationSubarachnoid hemorrhageImmunologyCell biology

Abstract

fetched live from OpenAlex

Despite marked advances in surgical techniques and understanding of secondary brain injury mechanisms, the prognosis of intracerebral hemorrhage (ICH) remains devastating. Harnessing and promoting the regenerative potential of the central nervous system may improve the outcomes of patients with hemorrhagic stroke, but approaches are still in their infancy. In this review, we discuss the regenerative phenomena occurring in animal models and human ICH, provide results related to cellular and molecular mechanisms of the repair process including by microglia, and review potential methods to promote tissue regeneration in ICH. We aim to stimulate research involving tissue restoration after ICH.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.038
GPT teacher head0.310
Teacher spread0.272 · 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; both teacher heads agree on what is shown here.

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

Citations33
Published2021
Admission routes2
Has abstractyes

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